Association between social factors and performance during Functional Capacity Evaluations: a systematic review
Bibliographic record
Abstract
Purpose: Determine the association of different social factors with Functional Capacity Evaluation (FCE) performance in adults.Materials and methods: A systematic literature search was performed in MEDLINE, CINAHL, and PsycINFO electronic databases. Studies were eligible if they studied social factor’s association with the performance of adults undergoing FCE. Studies were assessed on methodological quality and quality of evidence. The review was performed using best-evidence synthesis methods.Results: Thirteen studies were eligible and 11 social factors were studied. Considerable heterogeneity regarding measurements, populations, and methods existed among the studies. High quality of evidence was found for the association of FCE performance with the country of FCE and examiner’s fear behavior; moderate quality of evidence with previous job salary; and low or very low quality of evidence with compensation status, litigation status, type of instruction, time of day (workday), primary or mother language, and ethnicity. Other social factors were not studied.Conclusions: Evidence for associations of various social factors with FCE performance was found, but robust conclusions about the strength of the associations cannot be made. Quality of evidence ranged from high to very low. Further research on social factors, also within a biopsychosocial context, is necessary to provide a better understanding of FCE performance.Implications for RehabilitationResearch on Functional Capacity Evaluation (FCE) performance and its association with biopsychosocial factors have scarcely addressed the impact of social factors, limiting full understanding of FCE results.The social factors, healthcare (examiner’s fear behavior and type of instruction), personal or cultural systems (country of FCE, primary or mother language, and ethnicity), workplace system (previous job salary, time of day (workday)), and legislative and insurance system (compensation and litigation status), have a bearing in FCE performance.Better understanding of factors associating with functional capacity provide insights in FCE, allowing clinicians to improve the evaluations and interpretations of the assessment and better design the rehabilitation program.Better understanding of factors that influence FCE performance, and of unstudied factors, will allow researchers guidance to further investigate the construct of functional capacity.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".