A scoping review to characterize bridging tasks in the literature on aging with disability
Bibliographic record
Abstract
BACKGROUND: Bridging involves improving knowledge sharing and collaboration across different fields, such as aging and disability. The objectives of this review were to describe: 1) the contexts where bridging has occurred in relation to delivery of health services for adults aging with neurological or developmental conditions; and 2) characterize and map bridging tasks, stakeholders involved, and outcomes discussed in peer-reviewed literature. METHODS: Seven databases were searched around the core concepts of "bridging," "aging," and "disability." In total, 10,819 articles were screened with 49 meeting the inclusion criteria of discussing aging with developmental or neurological disability, explicitly describing bridging tasks, published in English and a peer-reviewed publication. Bibliographic information, sample characteristics, and data on bridging was extracted and included in the qualitative synthesis. RESULTS: Intellectual and/or Developmental disabilities were the most studied population (76% of articles), and most articles were published in the United States (57%). Twenty-two bridging tasks were identified, and categorized into three domains: health and social service delivery (e.g., care coordination tasks), policy (e.g., policy change), and research and training (e.g., mentoring). Stakeholders involved ranged from health care professionals to policy makers and organizations in aging and disability services. CONCLUSIONS: The resulting matrix will assist in the specification of bridging in research and practice. Future work should evaluate specific models of bridging and their effects on health service delivery.
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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 | 0.047 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.076 | 0.081 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".