Using Personal Health Records to Scaffold Perceived Self-Efficacy for Health Promotion
Bibliographic record
Abstract
According to Bandura (1977), believing in one's ability to achieve a goal is one of the best predictors that a goal will be accomplished. Given its predictive power, the concept of belief in one's ability to succeed, or perceived self-efficacy, is well researched for its influence on health promotion. It has been argued that a paradigm shift must occur away from illness treatment towards illness prevention and health promotion, for healthcare to accommodate the needs of the population. Personal Health Records (PHRs) may be a tool to help facilitate this paradigm shift. PHRs are repositories of information that individuals can use to access, manage, and share their personal health information. An extension of Bandura's model of self-efficacy will be presented here which identifies opportunities for PHRs to enhance perceived self-efficacy through mastery, social modeling, social persuasion, and physiological state. Bolstering self-efficacy through PHR tools will expand the utility of PHRs beyond self-management to also facilitate health promotion and illness prevention and gains in self-efficacy are also likely to transcend into other areas of consumers' lives.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".