Exploring use of the ICF in health education
Bibliographic record
Abstract
Purpose: Currently, little is known regarding use of the International Classification of Functioning, Disability and Health (ICF) in health education applications. Therefore, this review sought to examine the scope of work that has been conducted regarding the application of the ICF in health education. Methods: A review of the current literature related to use of the ICF in health education programs was conducted. Twelve electronic databases were searched in accordance with a search protocol developed by a health sciences librarian. In total, 17 878 records were reviewed, and 18 articles met the criteria for inclusion in this review. Results: Current evidence regarding use of the ICF in healthcare education revealed that program and participant properties can be essential facilitators or barriers to successful education programs. In addition, gaps in comprehensive outcome measurement were revealed as areas for future attention. Educational applications of the ICF are very much a work in progress as might be expected given the ICF’s existence for only a little over a decade. Conclusions: To advance use of the ICF in education, it is important to incorporate the measurement of both knowledge acquisition and behavior change related to ICF-based programs. Ultimately, widespread implementation of the ICF represents not only a substantial opportunity but also poses a significant challenge.Implications for RehabilitationICF integration in health education is a relatively new, but growing phenomenon that is primarily limited to developed educational and healthcare systems.When incorporating the ICF into health education initiatives, special consideration should be paid to both the intended audience and the method of delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".