Refinements of the ICF Linking Rules to strengthen their potential for establishing comparability of health information
Bibliographic record
Abstract
Purpose The content of and methods for collecting health information often vary across settings and challenge the comparability of health information across time, individuals or populations. The International Classification of Functioning, Disability and Health (ICF) contains an exhaustive set of categories of information which constitutes a unified and consistent language of human functioning suitable as a reference for comparing health information. Methods and results In two earlier papers, we have proposed rules for linking existing health information to the ICF. Further refinements to these existing ICF Linking Rules are presented in this paper to enhance the transparency of the linking process. The refinements involve preparing information for linking, perspectives from which information is collected and the categorization of response options. Issues regarding the linking of information not covered or unspecified within the ICF are also revisited in this paper. Conclusion: The ICF Linking Rules are valuable for enhancing comparability of health information to ensure that information is available in a consistent manner to serve as a foundation for evidence-based decision-making across all levels of health systems. The refinements presented in this paper enhance transparency in, and ultimately reliability of the process of, linking health information to the ICF. Implications for Rehabilitation The International Classification of Functioning, Disability and Health (ICF) constitutes a unified and consistent language of human functioning suitable as a reference for comparing health information. Comparability of information is essential to ensure that the widest range of information is available in a consistent manner for any decision-maker at all levels of the health system. The refined ICF Linking Rules presented in this article outline the method to establish comparability of health information based on the ICF.
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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 | 0.512 | 0.841 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".