Applications of the International Classification of Functioning, Disability and Health in goal-setting practices in healthcare
Bibliographic record
Abstract
PURPOSE: While evidence exists to support the adoption of collaborative goal-setting in healthcare, some clinicians feel that it is not feasible. This article proposes that the development of a standardized collaborative goal-setting approach using the framework and classification system of the International Classification of Functioning, Disability and Health (ICF) could address these feasibility issues. It is the objective of this article to understand the extent to which the ICF has been integrated into current goal-setting literature in order to assess its potential use in a standardized collaborative goal-setting approach. METHODS: A scoping review of the literature published in English since 2001 was conducted in EMBASE, Medline and CINAHL. Articles were included in this review if they integrated the ICF into goal-setting practices in any healthcare discipline. RESULTS: Nineteen articles were included in this review. Analysis of these articles revealed that the ICF has been integrated into goal-setting practices. The benefits associated with this integration suggest that integrating the ICF into goal-setting practices can standardize collaborative goal-setting. CONCLUSION: Evidence from this scoping review supports the use of the ICF in healthcare goal-setting practices because it provides clinicians and patients with specific steps to follow when attempting to set goals collaboratively. IMPLICATIONS FOR REHABILITATION: Collaborative goal-setting. Collaborative goal-setting involves patients working with their clinicians to develop health outcome goals together and is a fundamental component of patient-centered care. Some rehabilitation disciplines have yet to fully embrace collaborative goal-setting due to feasibility issues. This article proposes that the integration of the International Classification of Functioning, Disability and Health (ICF) into goal-setting practices could standardize collaborative goal-setting in a way that would address these feasibility issues.
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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 | 0.066 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".