Two Puzzles, a Tour Guide, and a Teacher: The First Cohorts’ Lived Experience of Participating in the MClSc Interprofessional Pain Management Program
Bibliographic record
Abstract
(1) Background: The Master of Clinical Science program (MClSc) in Advanced Healthcare Practice at (University) introduced a new "Interprofessional Pain Management" (IPM) field in September 2019. The purpose of this study is to inquire into the following research question: What are MClSc Interprofessional Pain Management students' lived experiences of participating in pain management education? (2) This study followed an interpretivist research design. The text that was considered central to descriptions of the lived experience of participating in the IPM program was highlighted and organized into a spreadsheet and then sorted into themes. (3) Results: Five themes in regard to the lived experiences of participating in the first cohort of the MClSc IPM program were identified: Reflection on Stagnation in Professional Disciplines; Meaning Making Through Dialogue with Like-Minded Learners; Challenging Ideas and Critical Thinking at Play; Interprofessionalism as Part of Ideal Practice; and Becoming a Competent Person-Centred Partner in Pain Care. (4) Conclusions: This program offers a unique approach to learning while creating an online platform to work, collaborate, and challenge like-minded experts in the field of pain. In doing this research, we hope that more practitioners will work towards the goal of becoming competent, person-centered pain care providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".