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 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 | 0.002 | 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 | 0.001 | 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 | 0.001 |
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 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".