University of McGill massive open online course: pioneering sport and exercise medicine education
Bibliographic record
Abstract
Abstract Background Animal-assisted intervention (AAI) programs, used widely for patient benefit, have increasingly been used for healthcare workers (HCW) to reduce occupational stress. However, there are barriers to these programs which limit their utilization, for both patients and HCW, specifically infectious disease concerns. The aim of the research project is to identify barriers and facilitators to AAI program use for healthcare worker benefit, and determine knowledge, beliefs, and practices regarding infectious disease risk and control policies, in order to understand the contextual parameters of program implementation. Methods We collected perceptions of key stakeholders involved with hospital AAI programs (HCW and AAI workers) through semi-structured in-depth interviews. We used framework analysis to guide thematic coding, completed independently by three researchers. Results We interviewed 37 participants. We divided our themes into two topic areas: program use for HCW and perceived infectious disease risk. Use for healthcare workers included perspectives on the benefits for HCW and program barriers and facilitators (specifically collaboration and leadership). Perceived risk included opinions on infection concerns with AAI, thoughts on control measures to reduce this risk, and responsibility for safety during these programs. Conclusions While significant benefits were reported for HCW, they were limited by administrative barriers and hazard concerns. Facilitators to surmount these barriers are best implemented with collaboration across the hospital and appropriate leadership roles to direct safe program implementation. By addressing these barriers through targeted facilitators in the form of evidence-backed guidelines, AAI programs can be used to benefit both patients and HCW.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.193 | 0.028 |
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".