Psychosocial determinants of wellbeing and discordance between a virtual-based functional outcome and actual performance
Bibliographic record
Abstract
Background Discordance between perceived and actual function reflects psychological well-being, in that possessing positive psychological qualities is associated with reporting less disability on self-reported outcomes compared with actual physical limitations. Considering the fast growing use of digital data in healthcare, assessment of virtual outcome tools’ ability to incorporate psychosocial elements of well-being is warranted. The purpose of this cross-sectional study was to examine the relationship between psychosocial indicators of well-being and the amount of discordance between a virtual-based outcome tool and a performance-based functional measure.Methods Patients with moderate to severe osteoarthritis (OA) of the hip or knee joint who were referred for consideration of joint replacement surgery completed a virtual-based performance measure outcome, a performance-based test and a psychosocial survey.Results Data of 123 patients, mean age, 68(8), 82(67%) females, 41(33%) males, 81 (66%) knees and 42 (34%) hips were included in the study. Higher negative affect (p = 0.002) and fear avoidance (p < 0.0001), and lower positive affect (p < 0.0001) were associated with less discordance with age, gender and severity of joint damage being accounted for. The female gender (p values ranging from 0.02 to 0.001) and severity of radiological pathology (p values ranging from p = 0.02–0.04) had an independent association with discordance.Conclusions The virtual-based measures have an ability of incorporating certain elements of psychosocial well-being such as positive and negative affect, and fear avoidance beliefs. Women and patients with more severe radiological pathology showed a more inferior pattern of discordance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".