Promoting Self-Care at School for Students With Chronic Health Needs
Bibliographic record
Abstract
Children with chronic health needs who have the capacity for self-care have been shown to have higher academic performance and greater social inclusion. However, self-care is rarely practiced as a focal point of health services interventions outside of clinical settings. Furthermore, little is known about how self-care develops through family, health, and educational support networks as a whole and from the point of view of teachers. This study used in-depth, semistructured interviews and a demographic questionnaire to generate teachers’ perspectives on the promotion of self-care in elementary school children with diabetes, and to represent their interactions with key supporters, including health care professionals, members of children’s families, and providers of educational services. Participants were recruited based on having at least one student with diabetes who received or was receiving care through a health services program designed to facilitate self-care in the schoolchildren. The teachers were sequentially selected from a Public School District in a midsized city in New Brunswick (Canada; N = 4) and two Boards of Education in a midsized Ontario city ( N = 4). Through a thematic analysis, teachers’ perspectives on their promotion of self-care in schoolchildren with diabetes were represented through five main interconnected themes: (a) teachers’ conceptions of self-care, (b) learning about health supports, (c) communication for self-care, (d) building inclusion, and (e) a difficult but manageable endeavor. Study limitations and future research implications are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".