key: cord-0696124-zl2wfqmd
authors: Roche, Stephanie D.; Odoyo, Josephine; Irungu, Elizabeth; Kwach, Benn; Dollah, Annabell; Nyerere, Bernard; Peacock, Sue; Morton, Jennifer F.; O'Malley, Gabrielle; Bukusi, Elizabeth A.; Baeten, Jared M.; Mugwanya, Kenneth K.
title: A one‐stop shop model for improved efficiency of pre‐exposure prophylaxis delivery in public clinics in western Kenya: a mixed methods implementation science study
date: 2021-12-12
journal: J Int AIDS Soc
DOI: 10.1002/jia2.25845
sha: 77550d5caf697b665c1224bbfd413cca7772ffff
doc_id: 696124
cord_uid: zl2wfqmd

INTRODUCTION: In public clinics in Kenya, separate, sequential delivery of the component services of pre‐exposure prophylaxis (PrEP) (e.g. HIV testing, counselling, and dispensing) creates long wait times that hinder clients’ ability and desire to access and continue PrEP. We conducted a mixed methods study in four public clinics in western Kenya to identify strategies for operationalizing a one‐stop shop (OSS) model and evaluate whether this model could improve client wait time and care acceptability among clients and providers without negatively impacting uptake or continuation. METHODS: From January 2020 through November 2020, we collected and analysed 47 time‐and‐motion observations using Mann–Whitney U tests, 29 provider and client interviews, 68 technical assistance reports, and clinic flow maps from intervention clinics. We used controlled interrupted time series (cITS) to compare trends in PrEP initiation and on‐time returns from a 12‐month pre‐intervention period (January–December 2019) to an 8‐month post‐period (January–November 2020, excluding a 3‐month COVID‐19 wash‐out period) at intervention and control clinics. RESULTS: From the pre‐ to post‐period, median client wait time at intervention clinics dropped significantly from 31 to 6 minutes (p = 0.02), while median provider contact time remained around 23 minutes (p = 0.4). Intervention clinics achieved efficiency gains by moving PrEP delivery to lower volume departments, moving steps closer together (e.g. relocating supplies; cross‐training and task‐shifting), and differentiating clients based on the subset of services needed. Clients and providers found the OSS model highly acceptable and additionally identified increased privacy, reduced stigma, and higher quality client–provider interactions as benefits of the model. From the pre‐ to post‐period, average monthly initiations at intervention and control clinics increased by 6 and 2.3, respectively, and percent of expected follow‐up visits occurring on time decreased by 18% and 26%, respectively; cITS analysis of PrEP initiations (n = 1227) and follow‐up visits (n = 2696) revealed no significant difference between intervention and control clinics in terms of trends in PrEP initiation and on‐time returns (all p>0.05). CONCLUSIONS: An OSS model significantly improved client wait time and care acceptability without negatively impacting initiations or continuations, thus highlighting opportunities to improve the efficiency of PrEP delivery efficiency and client‐centredness.

In the 6 years since the World Health Organization released guidelines recommending daily oral tenofovir-based preexposure prophylaxis (PrEP) for populations at substantial risk of HIV infection [1], 21 countries in sub-Saharan Africa (SSA) have implemented pilot or national PrEP programs [2] . From 2016 to 2020, these programs initiated 500,000 individuals on PrEP [2] ; however, epidemiologists estimate the current rate of PrEP uptake in SSA is still too slow to significantly impact population-level HIV incidence [3] . Drawing on lessons learned from differentiated service delivery (DSD) for antiretroviral treatment (ART) [4] , recent PrEP implementation efforts in SSA and globally have focused heavily on diversifying PrEP models in terms of delivery location, provider type, frequency of contact, and package of services offered [5, 6] . Many DSD models have moved PrEP delivery outside of clinical settings [4, 6] to circumvent barriers, such as long wait times, HIV stigma, insufficient privacy, and poor treatment from healthcare providers [7] [8] [9] [10] [11] . Less attention, however, has been given to improving existing models of PrEP delivery used in public healthcare facilities.

Recognizing that such facilities will play a key role in achieving PrEP at scale, some PrEP implementers have called for "PrEP delivery optimization" [12] [13] [14] [15] , including the Kenya Ministry of Health (MOH), which encouraged the development of "facility-based fast track models" [16, p. 61] in its latest strategic AIDS framework. Currently, in most public healthcare facilities, services are delivered sequentially by different providers at different delivery points (e.g. HIV testing services [HTS] delivered by HTS providers at HTS points; dispensing done by pharmaceutical technologists at the pharmacy) with clients moving between, and frequently queueing at, each. One promising option for optimizing PrEP delivery is a "one-stop shop" (OSS), a broad term for models that deliver all necessary services at a single touchpoint. Whereas some co-locate services so clients receive them "under the same roof," others further reorganize services so clients receive them in a single room and/or from a single provider [17, 18] . Regardless of OSS configuration, the underlying rationale is the same: to increase care acceptability among clients and improve efficiency by eliminating unnecessary wastes, such as queueing and movement. In SSA, OSS models have primarily been used to integrate previously separate service lines, like HIV and TB [19] [20] [21] [22] or family planning (FP) services [23] [24] [25] [26] [27] [28] . An OSS model that consolidates the core components of the PrEP intervention (HTS, counselling, clinical assessment, and drug dispensing) has yet to be implemented and evaluated within the context of routine PrEP delivery in SSA. As countries scale up PrEP, they may benefit from understanding how some, or all, of these steps might be consolidated and the impact on client care experiences. We, therefore, conducted a mixed methods study at four public healthcare facilities in Kenya to identify strategies for operationalizing an OSS model and assess whether this model could improve efficiency and care acceptability among clients and providers without negatively affecting uptake and continuation.

Beginning in 2017, the Partners Scale-Up Project (PSUP) catalyzed national scale-up of PrEP through provision of technical assistance (TA), initially to 25 clinics and later to over 100 clinics [29] . In August 2019, PSUP presented the OSS concept to MOH and clinical leaders of 12 PrEP clinics in Western province. Clinics interested in piloting an OSS submitted to PSUP a plan detailing how they would operationalize their OSS model (e.g. where they would set it up and which cadre of providers they would involve). From September to December 2019, PSUP facilitated meetings at prospective study sites to gather feedback from comprehensive care clinic (CCC), HTS, and pharmacy providers, which clinics subsequently used to refine their OSS proposals. PSUP also administered to staff a validated survey [30] to assess organizational readiness for change (ORIC). Ultimately, PSUP selected four clinics based on proposal strength, staff support for piloting an OSS, and ORIC scores. Like most PrEP facilities in Kenya, the four sites selected were subcounty hospitals that delivered PrEP in their HIV CCCs using national PrEP guidelines. The surrounding area of clinics A and B was peri-urban, whereas that of clinics C and D was rural. For this pilot, clinics A and B established their OSS in their differentiated care clinics (where stable clients receive express ART services) and discontinued PrEP delivery in their CCCs. Clinics C and D established their OSS in their FP clinic and gender-based violence (GBV) clinic, respectively, and in addition, continued to offer regular (non-OSS) PrEP services at their CCCs. Clinics made their OSS models fit for purpose. In all clinics' baseline OSS models, the following services and delivery-related tasks were slated to occur within a single room: client file retrieval, clinical review, adherence counselling, prescription writing, and dispensing. Clinic B and D's baseline models additionally featured in-room vital signs assessment. Lastly, whereas clinics A, B and C planned for OSS clients to obtain HIV testing at an HTS point located in the same building as the OSS, clinic D's baseline model featured in-room HIV testing. Table S1 of the appendix further details clinics' PrEP delivery models pre-and postintervention.

Our study used a convergent mixed methods design [31] , with quantitative and qualitative data collected simultaneously and given equal weight during analysis [32] . Our primary outcomes of interest were (1) implementation strategies (i.e., actions taken to implement the OSS); (2) implementation challenges; (3) client wait time (time spent waiting or walking to receive PrEP services), which we hypothesized would decrease from the pre-to post-intervention period, and (4) acceptability of PrEP services, which we hypothesized would improve under the OSS model. Although the OSS intervention was not intended to change time spent with a provider (contact time), PrEP initiations, or PrEP continuations, we assessed these as secondary outcomes. For our analysis of PrEP initiations and continuations, we selected four clinicstwo peri-urban and two rural -to serve as control clinics. These clinics had similar pre-intervention levels and trends in PrEP initiations and continuations as the four OSS clinics. We originally planned to launch the OSS in January 2020 and have a 12-month pre-intervention period (January-December 2019) and a 4-month post-intervention period (January-April 2020). Ultimately, we adjusted these accordingly for clinics C and D, which launched their OSS in February 2020, and to mitigate the influence of the COVID-19 pandemic, we expanded all clinics' post-intervention periods to November or December 2020 (depending on OSS launch date) and treated April through June 2020 as a wash-out period. This study was approved by the institutional review boards of the University of Washington (STUDY00002183) and the Kenya Medical Research Institute (P00040/3338), which did not require individual consent for client data collected as part of routine health services; written informed consent was obtained for interviews.

PSUP staff abstracted data on demographics (e.g., sex, age, and martial status), HIV risk factors and PrEP use (e.g., PrEP initiate date and refill history) from clinical records of individuals who initiated PrEP and/or received follow-up care from January 2019 through November 2020 and entered it into SurveyCTO (Dobility, Inc., Cambridge, MA, USA). At study baseline and endline, trained PSUP staff used a structured tool to conduct 47 time-and-motion observations of randomly selected PrEP initiation and follow-up visits at OSS clinics on different days of the week and times of day. Total service time was divided into contact time and wait time.

During the post-period, technical assistants conducted bimonthly clinic visits and generated clinic flow maps and reports (n = 69) on implementation strategies used and challenges encountered, model modifications, and OSS acceptability. In October and November 2020, Kenyan qualitative researchers (authors BK and AD) conducted interviews with (1) healthcare providers employed by an OSS clinic or the County Department of Health and (2) PrEP clients. Eligible individuals delivered or oversaw the delivery of OSS PrEP services (provider group) or obtained PrEP at an OSS at least once (client group), were age 18 or above, and self-reported comfort communicating in English. We anticipated that 12 client interviews (three per clinic) and 12 provider interviews (three per clinic) would be sufficient to answer our qualitative research questions, which were narrow in scope (e.g., to understand whether participants liked delivering/receiving PrEP services at the OSS). We used purposive sampling to recruit providers of different cadres and clients of different sexes, ages and exposure (yes/no) to the clinic's pre-OSS model. We developed semi-structured interview guides that solicited, from providers, details about OSS operations, barriers and facilitators, and perceived advantages and disadvantages, and, from clients, OSS visit descriptions, perceptions of care quality and recommendations for improvement. All interviews were conducted one-on-one, in English, in a private room or via phone, audio recorded and transcribed verbatim by the interviewer, with transcripts spot-checked for quality by author SDR.

Our primary outcome was client wait time, as captured in time-and-motion observations. We performed Mann-Whitney U tests to assess whether median wait time differed significantly from the pre-to post-periods. Secondary outcomes included clinic-level rates of PrEP initiation and percent of expected follow-up visits that occurred on time, with "on time"

defined as "occurring within two weeks of the date the client would run out of PrEP pills, according to dispensing records."

We assessed descriptive statistics of clients and collapsed data to obtain monthly counts of our outcomes. We compared OSS and control clinics using a controlled interrupted time series (cITS) approach. We modelled incidence rate ratios with negative binomial models with first-order autoregressive structure and included random intercepts and random slopes to account for clustering by clinic and clinic-level heterogeneity in intercepts and trends over time. Each model included fixed effects for study month, treatment group (intervention vs. control), number of months since OSS implementation and interactions for each pairwise combination to allow estimation of the pre-and post-implementation time trends and the immediate effect of implementation on the outcomes of interest. All quantitative analyses were conducted using RStudio (RStudio Team, version 1.4.999).

Interviews and TA reports were analysed using a combination of directed and conventional content analysis [33] . Our codebook included deductive codes for the types of waste in Ohno's model for continuous quality improvement, implementation strategies from a modified version of the Expert Recommendations for Implementing Change [34, 35] and change concepts identified by Langley et al. [36, 37] , as well as inductive codes identified during repeated readings of the data [38] . Author SDR drafted interview memos with a summary of key points for each code, quotations and analytic reflections [39] that drew comparisons across participants and datasets and synthesized findings into higher level themes [40] . Memos were reviewed by the interviewers, with disagreements resolved through group discussion. Simultaneous integration [41] of qualitative and quantitative data was further achieved through the development of joint displays to determine common concepts and explore how results confirmed, contradicted, or expanded upon one another [42] . To highlight the relationship between actions and improvement, we organized the identified implementation strategies according to change concepts compiled by Langley et al. [37] . We also categorized the specific implementation challenges clinics encountered according to the Tailored Implementation in Chronic Disease (TICD) checklist [43, 44] . Table S2 of the appendix contains additional details on our methodology.

During the 12-month pre-intervention period, intervention and control clinics initiated 385 and 212 clients on PrEP, respectively. During the 8-month post-intervention period, intervention and control clinics initiated 410 and 220 clients on PrEP, respectively. In both groups during both periods, approximately 60% of clients were female and about 75% were 18-to 34-years-old and in a known serodiscordant relationship (Table 1) . Intervention and control clinics had, (23) Multiple sex partners and no consistent condom use 45 (12) 13 (6) 28 (7) 13 (6) Intervention (14) 47 (20) 60 (18) 30 (19) Multiple sex partners and no consistent condom use 31 (8) 8 (3) 27 (8) 15 (10) a Pre-intervention period: 1 January 2019-31 December 2019 for all sites except two in the intervention group whose pre-intervention period end date is 14 February 2020. b Post-intervention period: 1 January 2020-30 November 2020 (excluding wash-out period of 1 April 2020-30 June 2020) for all sites except two in the intervention group whose post-intervention period start date is 15 February 2020.

respectively, 1276 and 620 follow-up visits during the preintervention period and 523 and 277 follow-up visits during the post-intervention period. Distributions of sex, age, and HIV risk factors were similar across groups and periods.

We interviewed 14 (Table S3 ).

From the pre-to the post-period, the average monthly number of PrEP initiations increased by 6 at OSS clinics (from 7.6 to 13.6) and by 2.3 at control clinics (from 4.5 to 6.8); results of cITS analyses revealed no significant difference between OSS and control clinics with respect to immediate change in initiations at the time of OSS implementation (p = 0.5) or over time (p = 0.4). From the pre-to the post-period, the average monthly percent of expected follow-up visits that occurred on time decreased by 18% at OSS clinics (from 70% to 52%) and by 26% at control clinics (from 70% to 42%); cITS analyses again found no significant difference between OSS and control clinics with respect to immediate change (p = 0.08) in this outcome or change over time (p = 0.6). (Figure 1 ). Analysis of clinic flow maps, TA reports and interviews suggests that clinics achieved this efficiency gain by operationalizing three main change concepts via five discrete implementation strategies (highlighted in bold below and detailed in Table 2 ).

Although a few providers noted occasional back-ups at the OSS, all providers agreed that, on average, moving PrEP delivery to a lower volume department (Change Service Sites) reduced client wait time:

[ Figure 2 further illustrates how changes to clinic and client flow eliminated stops at the registration, triage and pharmacy areas, thus reducing client movement and queueing.

In addition to the time and motion savings, most clients reported that this consolidation enhanced their privacy by reducing the number of providers they see and enabling them to skip the pharmacy. [At the OSS] they just give [PrEP] to you, you put it in your bag, and leave. No one will know I am taking PrEP because I don't have to go the pharmacy to queue with everyone else. (Female PrEP client, clinic C) Some providers reported that centrally locating PrEP supplies reduced their movement around the clinic and facilitated more accurate, in-flow PrEP documentation. Task-shifting resulted in some providers having more responsibilities (e.g., vital signs assessment and PrEP dispensing) under the OSS model than the pre-intervention model; yet, when asked how they felt about these additional tasks, providers reported that they did not present a significant burden, were worthwhile because they made clients happier, and, in some cases, made it easier for them to carry out their primary PrEP delivery responsibilities:

Previously 

In response to PrEP clients' complaints about being lumped together with ART clients, two clinics differentiated PrEP clients from others by fast-tracking PrEP clients to the front of a designated OSS clinician's queue (Obtain and Use Client Feedback). A third clinic implemented fast-tracking at the HTS point. Three clinics additionally differentiated clients requiring clinician attention (e.g., clients initiating PrEP) from those who did not (e.g., follow-up clients with no issues) by allowing counsellors to fully attend to the latter (Revise Professional Roles).

Early challenges (bolded hereafter and detailed in Table 3) included Scheduling and Delegation of Tasks at clinics A and B, both of which initially designated a single clinician to run the OSS and struggled when s/he was off service or busy with other clients. Clinics addressed this issue by implementing a rotating schedule so the OSS was always staffed and by task-shifting PrEP refill distribution from clinicians to peer educators. At clinic C (FP clinic), insufficient Domain Knowledge about PrEP delivery and a heavy FP Workload initially led some to resist delivering PrEP. In response, this clinic provided additional PrEP training and assigned two providers per shift to the OSS. Although clinic D (GBV clinic) originally planned to do in-room HIV testing, existing Regulation, Rules, and Policies made this infeasible, as there was no established pathway for the OSS to order testing kits from the clinic's store, and the HTS Department protested that HIV testing was only allowed in designated HTS points. At clinics A and B, as part of COVID precautions, the HTS point closest to the OSS was closed for most of the post-OSS period; thus, suboptimal Resource Proximity led to clients walking farther to receive HTS. Lastly, 6 months post-OSS implementation (July 2020), clinic B was designated a COVID isolation centre and had to relocate its OSS to the CCC. A few clients complained that this move failed to meet their Patient Preferences to receive PrEP services in a clinic area not associated with HIV care.

Whereas some provider interviewees expressed hope that the OSS would eventually increase PrEP initiations and improve continuation, a few viewed the primary value of the OSS as better meeting clients' care preferences, a benefit also reported by many clients. 

Delivery inefficiencies threaten to undermine the public health impact of PrEP by tempering both client willingness to access and continue PrEP and provider ability to deliver PrEP services at public health facilities. Although an increasing variety of private sector and/or non-facility-based delivery models are being tested in Kenya and other parts of SSA (e.g., PrEP delivery to adolescent girls and young women in community safe spaces [45] ; retail pharmacy-based PrEP delivery [46] ), public health facilities are currently the main purveyors of PrEP in SSA and will likely remain so as countries scale PrEP up nationally because of their potential reach.

Our study adds to the PrEP delivery science by identifying a basic change package of low-cost, easy-to-implement strategies that enabled public clinics to significantly reduce client wait time and improve care acceptability among clients and providers. The reported benefits of the OSS included not only less waiting time (queueing and movement) but also reduced stigma, enhanced privacy, and higher quality client-provider interactions. Though specific to Kenya, our findings may have broad applicability to other public health systems in SSA that have similarly been organized around delivering curative care through highly differentiated service lines [47] . Throughout SSA, PrEP is being added to public health systems that are already resource-constrained, resulting in challenges for provider buy-in [48] [49] [50] . Providers in our study, however, demonstrated their willingness to change how they deliver PrEP, even if this meant taking on additional work. Their emphasis on how the OSS made their clients happy suggests that clinicians were motivated, in part, by positive client feedback. This finding aligns with other qualitative studies with PrEP providers [51, 52] , as well as theories from behavioural and implementation science, which posit that provider willingness to adopt an innovation is driven, in part, by feelings of purpose [53] and belief that the innovation will confer a relative advantage [54] . Providers may also have been motivated to change their delivery practices because of the efficiency gains it created for them (e.g., less roomto-room movement), which freed up time for them to spend with other clients. Overall, our findings suggest that, even in resource-constrained settings, providers may be more willing to take on PrEP delivery when the model is efficient and person-centred. Ensuring that providers understand these benefits will likely be an important step for securing their support.

Task-shifting is a commonly used strategy for addressing human resource constraints across SSA [55] , especially for ART delivery [56, 57] . Similarly, the clinics in our study taskshifted specialized tasks "down" to lower level cadres, such as moving PrEP dispensing from pharmaceutical technologists to peer educators; however, contrary to prevailing practices, clinics also achieved efficiency gains by moving less specialized tasks "up" to higher level cadres. For example, at times, clinicians, instead of peer educators, took vital signs. Although in many contexts, task-shifting "up" would be considered a poor use of a rare resource (a clinician), our study found that, in the context of highly fragmented service delivery and unreliable wait times at other service delivery points, taskshifting "up" simple tasks with short cycle times makes sense as an improvement strategy. This strategy also worked well in this context because clinics relocated all necessary supplies to the OSS room, thereby ensuring that the time OSS providers spent with clients was predominantly "value-add" and not wasted searching for or retrieving materials from other clinic areas. In short, task-shifting "up" in this context corrects for some of the negative consequences of an organizational structure that prioritizes differentiation by function over coordination of functions [58] . Our finding that PrEP clients strongly prefer to see fewer providers also aligns with prior studies on ART client care preferences [59] [60] [61] . Future research is needed to investigate the impact of task-shifting "up" on PrEP clinician productivity and to evaluate the acceptability of alternative OSS models. In light of our finding that over half of client wait time was for travel to/from and queueing at an HTS point, future iterations of the OSS model should test additional interventions, such as HIV self-testing, that could potentially expedite the HTS component. For example, a recent study at a subcounty hospital in Western province piloted the use of in-room, oral fluid-based HIV self-testing (HIVST) for PrEP continuation and found that clients who opted for HIVST had significantly shorter clinic visits [62] . A randomized controlled trial currently underway in Central province is assessing whether dispensing clients a 6-month supply of PrEP and allowing them to complete quarterly HIV testing at home via an oral fluid-or blood-based HIVST leads to better adherence and continuation [63] .

In African ART programs, DSD models for stable and notyet stable ART clients have emerged, in part, because client groups do not require the same subset of services [64] . Clinics in our study incorporated differentiation into their OSS models by separating new PrEP initiators from those coming for PrEP refills. By building workflows around the different types of clients and their sets of needs, the OSS model created greater predictability in service times. Whereas high variation in service times often lowers the acceptability of public facility-based services [65-67], the PrEP clients in our study expressed strong acceptance of a PrEP delivery model that featured shorter, more consistent wait times. Importantly, clinics reduced variation in service time without any additional human resources, making the OSS model a promising option for PrEP programs working within limited fiscal space.

Overall, the OSS model achieved its intended objectives, which were to improve efficiency of service delivery and care acceptability. As expected, we did not observe a significant change in provider contact time, initiations, or continuations. The OSS intervention that we tested did not entail any changes to the number or content of PrEP component services; however, it is possible that some portion of contact time is non-value-add, and future research should assess whether and how contact time could be reduced without compromising care quality. Our OSS intervention also did not entail a demand creation component (e.g., study clinics did not advertise OSS services). As such, we were not surprised to see no significant change in initiations; however, we recognize that increasing the number of clients with HIV risk who initiate PrEP will be important for maximizing PrEP's public health impact on population-level HIV incidence. Additional research is needed to understand whether and how the OSS's gains in care acceptability (e.g., greater privacy) can be parlayed into more individuals initiating PrEP. Lastly, as we expected for a study of our size and duration, we did not observe a significant change in PrEP continuation. Additional research is needed to understand whether an OSS model affects PrEP continuation in the long term.

Our study has limitations. We interviewed English-speaking providers and clients willing to deliver or obtain OSS PrEP services at a public clinic in western Kenya; their perspectives may not generalize to other providers/clients and PrEP clinics in other provinces of Kenya. We did not collect quantitative data on clinics' fidelity to their OSS model. Future research should capture this information to understand at what level of fidelity the model needs to be executed to achieve the same outcomes. Most clients obtained follow-up care at the OSS only once; it is possible that our post-intervention period was not long enough to capture a lagged effect on continuation.

For PrEP to succeed as a public health intervention, it not only needs to be available at scale, but also used by the target population with sufficient rates of uptake, persistence and adherence [68] . An OSS approach to PrEP delivery may be useful for obtaining provider buy-in and making care more patient-centred.

JMB is an employee of Gilead Sciences. For the remaining authors, none were declared. 

We would like to thank the PrEP clients and providers who participated in this study. We would also like to acknowledge the Director General of the Kenya Medical Research Institute and the Director of the Center for Microbiology Research for their administrative support.

The Partners Scale-Up Project is funded by the National Institute of Mental Health of the US National Institutes of Health (grant R01 MH095507) and the Bill & Melinda Gates Foundation (grant OPP1056051).

Data are available by contacting the International Clinical Research Center at the University of Washington (icrc@uw.edu).

1. World Health Organization. WHO expands recommendation on oral preexposure prophlaxis of HIV infection (PrEP). Geneva: World Health Organization; 2015.

Additional information may be found under the Supporting Information tab for this article: Table S1 . Study clinics' PrEP delivery models pre-and postimplementation of the One-Stop Shop (OSS) intervention Table S2 . Consolidated criteria for reporting qualitative studies (COREQ) checklist Table S3 . Demographic characteristics of interview participants

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