Validation and psychometrics for the Health Skills Profile
Bibliographic record
Abstract
INTRODUCTION: Few measures can comprehensively explore the extent to which individuals are able to effectively identify areas of concern, create a personalized health action plan and target these areas for improvement. Thus, the aim of this paper was to validate the Health Skills Profile (HSP©) as a measure for assessing health-related skills and explore the relationship between the HSP skills with existing validated measures. METHOD: Participants completed a battery of self-report measures (including validated measures and visual analogue scales [VAS] that relate to each of the health-related skills) and the HSP measure online. RESULTS: We explored the association between each skill within the HSP with their corresponding validated measure. We found a significant positive relationship between the HSP skills and the validated measures. Further, we found a significant positive relationship between the HSP skills and the corresponding VAS. CONCLUSION: These findings suggest that the HSP can be combined with other assessment data to develop more complete personalized profiles of individual and organizational health and health behaviors.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".