Systematic ethnography of school‐age children with bleeding disorders and other chronic illnesses: exploring children's perceptions of partnership roles in family‐centred care of their chronic illness
Bibliographic record
Abstract
PURPOSE: In this qualitative study, school-age children between 7 and 11 years, living with a bleeding disorder or other chronic illness, defined how they understand their partnership role in family-centred care (FCC), and recommended FCC strategies. METHODS: This systematic ethnography had three phases: unstructured interviews explored how children understood FCC partnership roles (phase I); document review provided FCC institutional context (phase II); and validation interviews generated FCC partnership recommendations (phase III). This study took place within the area served by a Western Canadian children's hospital. Interviews were held at the hospital or children's home. In phase I, purposive sampling was used to recruit children receiving outpatient care for a bleeding disorder (n = 4) or another chronic illness (n = 4). In phase II, policies and legislation were compared with how children understand their FCC partnership role. In phase III, validation interviews were conducted with children (n = 3) to confirm domains and generate FCC supporting strategies. Data collection and analysis were based on domain analysis and qualitative ethnographic content analysis. RESULTS: Phase I outcomes included seven domains regarding how children understood their role as partners in FCC: my best interests, virtues, talking and listening, being involved, knowing, making decisions and being connected. Phase II outcomes revealed how these domains were represented in institutional contexts. Phase III outcomes confirmed domains and generated key strategies to support children's role as FCC partners through graphic representation of FCC as a treasure map, interactive workshop and online game. CONCLUSIONS: School-age children, living with a bleeding disorder or other chronic illness, similarly want to learn how to be FCC partners by developing competence as healthcare team members, identifying their own best interests, learning how to communicate with 'grown-ups', clarifying roles and goals, and guided opportunities for decision making. Facilitating school-age children's FCC partnership roles is worthy to explore in youth transition programmes.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".