A Qualitative Study of Community-Based HIV/AIDS Prevention Interventions, Programs, and Projects for Rural and Remote Regions in Canada: Implementation Challenges and Lessons Learned
Bibliographic record
Abstract
CONTEXT: Fifteen percent to 20% of the Canadian and American populations live outside urban areas, and despite growing regional HIV/AIDS-related health disparities, there is little published research specific to rural or remote (rural/remote) HIV/AIDS prevention programming. OBJECTIVE: To document implementation challenges, lessons learned, and evaluation approaches of promising and proven HIV/AIDS prevention programs and interventions developed and delivered by organizations with rural/remote catchment areas in Canada to provide a foundation for information sharing among agencies. DESIGN: Qualitative study design, using a community-based participatory research approach. We screened Canadian community-based organizations with an HIV/AIDS prevention mandate to determine whether they offered services for rural/remote populations and invited organizational representatives to participate in semistructured telephone interviews. Interviews were audio-recorded and transcribed. Content analysis was used to identify categories in the interview data. SETTING: Canada, provinces (all except Prince Edward Island), and territories (all except Nunavut). PARTICIPANTS: Twenty-four community-based organizations. RESULTS: Screening calls were completed with 74 organizations, of which 39 met study criteria. Twenty-four (62%) interviews were conducted. Populations most frequently served were Indigenous peoples (n = 13 organizations) and people who use drugs (n = 8 organizations) (categories not mutually exclusive). Key lessons learned included the importance of involving potential communities served in program development; prioritizing community allies/partnerships; building relationships; local relevancy and appropriateness; assessing community awareness or readiness; program flexibility/adaptability; and addressing stigma. Evaluation activities were varied and used for funder reporting and organizational learning. CONCLUSIONS: Rural/remote HIV/AIDS programs across Canada expressed similar challenges and lessons learned, suggesting that there is potential for knowledge exchange, and development of a community of practice. Top-down planning and evaluation models may fail to capture program achievements in rural/remote contexts. The long-term engagement practices that render rural/remote programs promising do not always conform to planning and implementation requirements of limited funding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".