Healthcare professionals’ perspectives on optimizing pain care-related education at a Canadian children’s hospital: A qualitative study
Bibliographic record
Abstract
Objectives: Pain affects all children and youth, yet acute and procedural pain remains undertreated in Canadian hospitals. To improve pain management practices in paediatric hospitals, it is necessary to understand how healthcare professionals (HCPs) wish for educational interventions to be designed to improve their pain management practice. Methods: Semi-structured interviews were conducted with 18 HCPs between October and December 2020. Snowball sampling was used to first recruit interested members from the hospital's Pediatric Pain Management Committee. Interviews were conducted per participant preference and included Zoom, telephone, and in-person interviews. Recruitment ceased when data were determined sufficiently rich. A thematic analysis of verbatim transcripts and reflexive field notes were used to create a data set focused on knowledge mobilization and clinical education. Results: Three core themes were identified: (a) the necessity for just-in-time education for HCPs; (b) the availability of clinical pain champions to educate staff; and (c) the provision of resources to educate children and their families about available pain management interventions. Just-in-time education included suggestions for in-service training, hands-on training, and regular updates on the latest research. Pain champions, including clinical nurse educators, were stressed as being important in motivating staff to improve their pain management practices. Participants noted the lack of resources for patient and family education on pain management and suggested providing more multimodal resources and educational opportunities. Conclusion: Having local champions introduce pain management initiatives and just-in-time education positively impacts the implementation climate, which also helps HCPs provide evidence-based education and resources to patients and families.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".