Adaptive Mentoring Networks and Compassionate Care: A Qualitative Exploration of Mentorship for Chronic Pain, Substance Use Disorders and Mental Health
Bibliographic record
Abstract
This study undertook an exploration of how Adaptive Mentoring Networks focusing on chronic pain, substance use disorders and mental health were supporting primary care providers to engage in compassionate care. The study utilised the Cole-King & Gilbert Compassionate Care Framework to guide qualitative semi-structured interviews of participants in two Adaptive Mentoring Networks in Ontario, Canada. Fourteen physician participants were interviewed including five mentors (psychiatrists) and nine mentees (family physicians) in the Networks. The Cole-King & Gilbert Framework helped provide specific insights on how these mentoring networks were affecting the attributes of compassion such as motivation, distress-tolerance, non-judgement, empathy, sympathy, and sensitivity. The findings of this study focused on the role of compassionate provider communities and the development of skills and attitudes related to compassion that were both being supported in these networks. Adaptive Mentoring Networks can support primary care providers to offer compassionate care to patients with chronic pain, substance use disorders, and mental health challenges. This study also highlights how these networks had an impact on provider resiliency, and compassion fatigue. There is promising evidence these networks can support the “quadruple aim” for healthcare systems (improve patient and provider experience, health of populations and value for money) and play a role in addressing the healthcare provider burnout and associated health workforce crisis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".