Implementation Process of a Canadian Community-based Nurse Mentorship Intervention in HIV Care
Bibliographic record
Abstract
We describe salient individual and organizational factors that influenced engagement of registered nurses in a 12-month clinical mentorship intervention on HIV care in Canada. The intervention included 48 nurses and 8 people living with HIV (PLWH) who were involved in group-based and one-on-one informal mentorship informed by transformative learning theory. We evaluated the process of implementing the mentorship intervention using qualitative content analysis. The inclusion of PLWH as mentors, the opportunities for reciprocal learning, and the long-term commitment of individual nurses and partner organizations in HIV care were major strengths. Challenges included the need for multiple ethical approvals, the lack of organizational support at some clinical sites, and the time commitment required by participants. We recommend that clinical mentorship interventions in HIV care consider organizational support, adhere to the Greater Involvement of People Living with HIV/AIDS principles, and explore questions of professional obligations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".