Evaluating family‐centred care at BC Children's Hospital: Healthcare providers' perspectives
Bibliographic record
Abstract
BACKGROUND: Family-centred care (FCC) emphasizes a partnership approach to care between healthcare providers (HCPs), patients, and families. FCC provides significant benefits to both children and families; however, challenges exist in implementing FCC into practice. This study aimed to explore HCPs' FCC behaviours in multidisciplinary specialty clinics at a tertiary pediatric health care center in Canada. METHODS: A demographic survey and the Measure of Processes of Care for Service Providers (MPOC-SP) questionnaire was administered to HCPs in five specialty clinics: orthopaedics, neurology, multi-organ transplant, immunology, and nephrology. Survey data were summarized using descriptive statistics. The association between possible predictor variables (ethnicity, gender, years in clinical practice) and MPOC-SP scores were analysed by analysis of variance (ANOVA) followed by post-hoc Tukey's test. Differences in scores across professional disciplines were analysed by multivariate analysis of variance (MANOVA) followed by ANOVA. Items rated lower (1-4 out of 7 by >33% of participants) were identified as potential areas for improvement. RESULTS: HCPs (N = 77) from all five clinics rated the MPOC-SP domain 'Treating People Respectfully' the highest (mean 6.00 ± 0.59) and 'Providing General Information' the lowest (mean 4.56 ± 1.27). HCPs with 5-10 years of experience had higher scores across all domains compared to HCPs with <5 years and >10 years of experience. There were no significant differences in scores based on ethnicity, gender, and professional discipline. Items rated lower (1-4 out of 7 by >33% of participants) involved providing general information and emotional support to families. CONCLUSIONS: Providing general information and emotional support to patients and families are areas for improvement for all specialty clinics surveyed. Given genetic counsellors (GCs) expertise in education and counselling, GC integration in these clinics is one way in which FCC can be improved. Our study also shows that years of work experience influences HCPs' capacity to provide FCC.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".