Developmentally appropriate patient education during transition: A study of healthcare providers’ and parents’ perspective
Bibliographic record
Abstract
Introduction: Patient education is recommended to improve the transition from paediatric to adult care for young people with chronic conditions. But a consensus has not been reached regarding a particular model. This study aimed to understand how to prepare for the implementation of a Developmentally Appropriate Patient Education during Transition (DAPET), which would revolve around the young person’s psychosocial development. Method: Three focus groups were organised with healthcare providers and two focus groups took place with the parents of young people with chronic conditions. We used activity theory to explore practices and to identify obstacles to the implementation of DAPET, as well as to recognise which resources might be available to implement DAPET. Results: Healthcare providers agreed on the need to engage in an educational approach centred on the psychosocial development of young people during transition. However, study findings highlight the following obstacles to doing so: a lack of competencies in adolescent and young adult medicine and a lack of available resources to meet these goals. Furthermore, parents wanted to redefine their role in the transition process and to allow their children to develop self-management skills. Conclusion: Healthcare providers and parents considered the implementation of DAPET to be acceptable and even advisable. However, the programme’s feasibility was questioned due to perceived shortfalls in the hospital system as it currently stands and the ways in which an educational approach would be applied. An environment that facilitates healthcare providers’ educational initiatives and encourages the participation of parents is required.
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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 | 0.010 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".