The potential contribution of critical theories in healthcare transition research and practice
Bibliographic record
Abstract
BACKGROUND: Healthcare transition has been established as a significant topic of interest in pediatric rehabilitation. Healthcare transition research has primarily focused on barriers to self-management and achievement of a productive adulthood. Healthcare transition experts have recently called for further attention to social structural factors. Theoretical approaches are, therefore, needed to account for how such factors shape the lives of youth with disabilities, particularly those who experience marginalization and social exclusion. PURPOSE: Taking up this call, the aim of this paper is to examine the potential contributions of two critical theories to healthcare transition research and practice. METHODS: Review two theories - intersectionality and critical discourse analysis. RESULTS: Intersectionality highlights how multiple intersecting social locations and social structures interact with youth's experiences, choices and health care needs. Critical discourse analysis focuses on how discourses and assumptions in healthcare transition research and practice contribute to marginalization and can be resisted and changed by youth, families, researchers, and clinicians. CONCLUSIONS: The uptake of critical theories within health care transition research and practice can account for the complex interplay of social structures, power relations and youth's experiences. Such analysis can contribute to refining assessments and developing interventions that reflect how marginalization and exclusion impact youth's well-being.IMPLICATIONS FOR REHABILITATIONWhile critical theories have been applied in health and rehabilitation, there has been limited uptake of these theories in healthcare transition research and practice.Critical theories can promote awareness of how youth's experiences, choices and actions throughout the healthcare transition process are shaping and shaped by structural factors and assumptions about a productive adulthood.Applying critical theories in healthcare transition practice involves being responsive to the structural factors that may be shaping youth's experiences, choices and opportunities.Intersectional and critical discourse analyses can surface how to reduce social exclusion and marginalization for youth transitioning to adulthood through analyses of language, power, dominant discourse and practices amenable to change.
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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 | 0.157 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.015 | 0.153 |
| Scholarly communication | 0.030 | 0.047 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".