Systems thinking perspectives applied to healthcare transition for youth with disabilities: a paradigm shift for practice, policy and research
Bibliographic record
Abstract
BACKGROUND: Healthcare transition (HCT) for youth with disabilities is a complex phenomenon influenced by multiple interacting factors, including health, personal and environmental factors. Current research on the transition to adulthood for disabled youth has primarily focused on identifying these multilevel factors to guide the development of interventions to improve the HCT process. However, little is known about how this complex array of factors interacts and contributes to successful HCT. Systems thinking provides a theoretically informed perspective that accounts for complexity and can contribute to enhanced understanding of the interactions among HCT factors. The objective of this paper is to introduce general concepts of systems thinking as applied to HCT practice and research. METHODS: Several systems thinking concepts and principles are introduced and a discussion of HCT as a complex system is provided. Systems dynamics methodology is described as one systems method for conceptualizing HCT. A preliminary systems dynamics model is presented to facilitate discourse on the application of systems thinking principles to HCT practice, policy and research. CONCLUSIONS: An understanding of the complex interactions and patterns of relationships in HCT can assist health policy makers and practitioners in determining key areas of intervention, the impact of these interventions on the system and the potential intended and unintended consequences of change. This paper provides initial examination of applying systems thinking to inform future research and practice on HCT.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".