Linking health and health-related information to the ICF: a systematic review of the literature from 2001 to 2008
Bibliographic record
Abstract
INTRODUCTION: In 1976, the World Health Organization (WHO) estimated worldwide disability prevalence at 10%; recent evidence suggests the prevalence is even higher. Given the extent of disability around the world, it is essential for researchers and policy makers to have a uniform language for describing and discussing disability. The International Classification of Functioning, Disability and Health (ICF) is WHO's attempt to provide that standard language. Linking rules were published in 2002 and 2005 suggesting a method for standardising the process of connecting outcome measures to the ICF classification. The objective of this study is to study the extent to which the linking rules have been used by researchers to link health and health-related information to the ICF and collect the feedback about the current practices, applications and areas to improve the linking method. METHOD: Using a systematic review of health-based literature between 2001 and February 2008, we (1) determined research areas where the linking method is applied, (2) examined the characteristics of studies that linked information to the ICF and (3) described current practices and issues related to the process of linking health and health-related information to the ICF both quantitatively and qualitatively. RESULTS: The systematic review yielded 109 articles from 58 journals that linked health information to the ICF and 58 of the articles employed published linking rules. The majority of articles were descriptive in nature, used linking for connecting content of health instruments to the ICF and linked English health content. Quality controls such as reliability checks, multiple raters and iterative linking processes were found frequently among users of the linking rules. Qualitative analysis created themes about: preparing units of information, who links to the ICF, reliability, matching or translating concepts from text to ICF categories, information unable or difficult to capture, quantitative reporting standards and overall linking process. DISCUSSION: This review also shows that the linking process is a useful way to apply the ICF classification in research. With over 100 articles published in 58 peer-reviewed journals across 50 focus areas, linking health and health-related information to the ICF has been shown to be a useful tool for describing, comparing and contrasting information from outcome measures used to collect quantitative data, qualitative research results and clinical patient reports across diagnoses, settings, languages and countries.
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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 | 0.049 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.052 | 0.053 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".