Healthcare Professionals’ Perspectives on Improving Family-Centred Pain Care in a Tertiary Pediatric Centre
Bibliographic record
Abstract
BACKGROUND: Despite being a core component of family-centered and compassionate care, children's pain is often undertreated in Canadian hospitals. Nurses' and other healthcare professionals' (HCPs) ability to understand and respond to a child and their family's pain care needs is integral to improving this care in a family-centered manner. PURPOSE: To understand nurses' and other HCPs' perceptions of child and family needs to make care more collaborative and patient- and family-centered. METHODS: = 4); 3 of the administrators had a nursing background. Transcripts were analysed using a semantic, inductive approach with two coders using a codebook to ensure reliability. RESULTS: Participants felt that pain care was important, but that it needs to take greater priority in the hospital. In our analysis, we identified four core needs that nurses and other HCPs have to provide better pain care: 1. Better acknowledgement of child and family experiences; 2. Better visual and written knowledge translation tools for patients and families; 3. Better provision of verbal pain education to children and families by nurses and other HCPs; and 4. Help for patients and families to advocate for better pain care when they feel their needs are not being met. CONCLUSIONS: Nurses and other HCPs value patient- and family-centered pain care, and wish to empower families to advocate for it when it is sub-optimal.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".