Factors that influence engagement in collaborative practice: how 8 health professionals became advocates.
Bibliographic record
Abstract
OBJECTIVE: To generate hypotheses regarding factors that might influence engagement in collaborative practice. DESIGN: Qualitative study using in-depth interviews. SETTING: Participants interviewed each other in dyads. The pairing was based upon geographical location and proximity to each other. PARTICIPANTS: Eight professionals from the disciplines of medicine, nursing, occupational therapy, physical therapy, and massage therapy. METHOD: Semistructured interviews, lasting 30 to 45 minutes each, were recorded and transcribed verbatim. The transcripts were read by all research team members using independent content analysis for common words, phrases, statements, or units of text for key themes. At a subsequent face-to-face meeting, the team used an iterative process of comparing and contrasting key themes until consensus was reached. The transcripts were then analyzed further for subthemes using NVivo software. MAIN FINDINGS: Initial findings suggest that some common characteristics grounded in family history, school experiences, social interactions, and professional training might influence collaborative practice choices. The narrative form of the interview broke down interpersonal and interprofessional barriers, creating a new level of trust and respect that could improve professional collaboration. CONCLUSION: This study suggests that life experiences from childhood into later adulthood can and do influence professional choices.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 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".