Innovative Programs with Multi-Service Integration for Children and Youth with High Functional Health Needs
Bibliographic record
Abstract
The integration of care services and providers across the health-social-community continuum has helped improve the lives of many children and youth living with complex health conditions.Using environmental scan data, 16 promising multi-service programs were selected and analyzed qualitatively through a deliberative conversation approach.Descriptive data of analyzed programs are presented, as well as the thematic analysis results.An important program strength is its clear founding principles and engagement of patients and families.However, the scale-up of these initiatives remains a challenge unless such programs can be better financed and supported. RésuméL'intégration des services et des fournisseurs de soins dans l' ensemble du continuum « services de santé-services sociaux-services communautaires » contribue à améliorer la vie de nombreux enfants et jeunes aux prises avec des problèmes de santé complexes.À l' aide de données d' analyse du contexte, 16 programmes multiservices prometteurs ont été sélectionnés et analysés qualitativement au moyen d' une approche de conversation délibérative.Les données descriptives des programmes analysés sont présentées ainsi que les résultats de l' analyse thématique.Une des forces importantes du programme consiste en ses principes fondateurs clairs et en l' engagement des patients et des familles.Cependant, l'intensification de ces initiatives demeurera un défi, à moins que de tels programmes puissent être mieux financés et soutenus.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".