Exploring the role of contextual factors in disability models
Bibliographic record
Abstract
PURPOSE: The objective of this paper is to define and categorize the types of relationships that contextual factors have within models of disability according to the WHO International Classification of Disability, Functioning, and Health (ICF) conceptual scheme. METHOD: A conceptual analysis building on the disability literature specifies the causal relationships for contextual factors in relation to the association between activity limitation and participation using a person with arthritis as an example. RESULTS: From a statistical point of view, in relation to disability process, contextual factors can act as an independent factor, confounding factor, moderating factor, and mediating factor. How the role of a particular contextual factor is specified depends on the researcher's hypothesized disability framework and research goals. Moderating and mediating contextual factors are of particular importance in disability model specification. Various sub-types of moderating contextual factors are also identified. CONCLUSION: This paper provides a framework for the conceptualization of contextual factors in the examination of disability models. This framework has implications in constructing conceptual models as well as for setting up analytical plans. In light of the increasing awareness and application of the ICF model, we intend this work to stimulate additional discussion on this topic.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".