Rehabilitation in the context of HIV: an interprofessional multi-stakeholder process for curriculum development.
Bibliographic record
Abstract
UNLABELLED: With longer survival, individuals living with human immunodeficiency virus (HIV) infection are facing a multitude of health-related challenges due to HIV, its associated concurrent health conditions, and treatments. Despite the need for rehabilitation, few rehabilitation professionals work with people living with HIV, with many feeling they lack adequate knowledge and skills to assess and treat this population. PURPOSE: We describe a national multi-stakeholder consultation used to inform the development of an interprofessional curriculum for rehabilitation professionals on HIV/AIDS. METHODS: We conducted a series of focus groups and key informant interviews (either in person or by telephone) with people living with HIV, rehabilitation professionals, physicians, curriculum experts, and other HIV stakeholders. Participants were asked to describe their perceived learning needs of rehabilitation professionals and to identify relevant content and delivery methods for a future interprofessional HIV/AIDS curriculum. RESULTS: Seven focus groups and 31 interviews with a total of 74 key informants were conducted, resulting in recommendations for content to include in HIV rehabilitation professional curricula and ways to deliver these curricula effectively. CONCLUSIONS: A national multi-stakeholder environmental scan was a useful preliminary step to inform the development of an interprofessional curriculum for rehabilitation professionals on HIV/AIDS. Recommendations serve as scaffold from which to build content and delivery of future curricula.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".