An HIV mentorship programme for rehabilitation professionals: lessons learned from a pilot initiative
Bibliographic record
Abstract
Aims: This article describes a pilot programme aiming to identify the strengths and challenges associated with a human immunodeficiency virus (HIV) mentorship programme for rehabilitation professionals. Methods: This unique pilot programme incorporated elements of problem-based learning, interprofessionalism and multiple mentoring models. A group of five physiotherapy mentees met initially with three clinician mentors, and three mentors who were persons living with HIV, for a one day workshop to develop relationships, identify learning needs and engage in learning through discussion of case scenarios. Subsequently they met formally every month via teleconference, over a five-month period, followed by a final one-day workshop. Qualitative evaluation included interviews and focus group methodology. Transcripts from these were analyzed through qualitative content analyses. Findings: Participants viewed the mentorship programme positively, in particular the networks they were able to establish, and learning from the mentors living with HIV. However, barriers to participation were related to scheduling and lack of support to devote time to the programme from the mentees' clinical managers. Other challenges related to the mentees' limited opportunities to implement new knowledge, and to difficulties in engaging in informal mentorship activities. Conclusions: Lessons learned from this pilot programme are of value to those interested in developing HIV-related mentorship experiences for rehabilitation health professionals or for those in other emerging areas of practice.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".