Pediatric Adherence as a Multidimensional and Dynamic Construct, Involving a Triadic Partnership
Bibliographic record
Abstract
OBJECTIVE: The aim of this paper is to integrate and draw attention to research findings that support our conceptualization of adherence as being multidimensional, dynamic, and involving a triadic partnership. METHODS: A review of relevant articles found in Medline, PsychLit, and reference lists provided in pertinent articles was performed. RESULTS: Significant progress has been made in identifying disease-related and psychosocial correlates of pediatric adherence and in developing intervention programs to promote adherence. Both lines of work have proceeded without a unifying framework for conceptualizing and measuring pediatric adherence. We offer a model that views adherence as comprising three defining aspects: (a) multidimensional, in support of the complexity and interrelatedness of treatment components, (b) a triadic partnership, as in mutually influential exchanges within and among the caregiver-medical team, child-medical team, and caregiver-child relationships, and (c) dynamic in relation to the notion of changes in developmental adaptive capacity, contextual characteristics, and disease course. CONCLUSIONS: Much work has yet to be done to validate, refine, and extend our model of adherence. Recommendations are offered for assessing each defining aspect, and a group-based methodology that accommodates longitudinal, prospective data is described.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".