Measuring professional self and collective efficacy for dietary advice in Primary Health Care
Bibliographic record
Abstract
BACKGROUND: There is a lack of instruments to measure the ability of health professionals to promote dietary advice according to dietary guidelines. AIM: To develop and validate a web-based and self-applied scale for measuring primary health care (PHC) professionals' self-efficacy and collective efficacy to apply the Brazilian Dietary Guidelines for dietary advice. METHODS: Methodological procedures comprised six steps: development of the items; content validation with panel of experts; face validation through focus group conducted with PHC professionals; online reevaluation by the participants of content and face validation panels; online application with PHC professionals working all over Brazil's macro-regions; confirmatory factorial analysis to test construct validity. RESULTS: The scale was initially developed with 22 items. After content and face validation panels, changes in content and semantics were performed. The second version consisted of 24 items equally divided into part A (self-efficacy) and B (collective efficacy). All items, when reevaluated, were considered clear and representative of the Brazilian Dietary Guidelines' chapters. The multidimensional model was shown to have excellent fit indices in the confirmatory factorial analysis. The scale's peak of information was centered around the mean, indicating that both domains are more precise for perception of self-efficacy and collective efficacy on average values. CONCLUSION: The scale demonstrated validity for measuring PHC professionals' perceived self-efficacy and collective efficacy to apply the Brazilian Dietary Guidelines. To our knowledge, this is the first valid scale for measuring the capability of PHC professionals to apply national dietary guidelines for healthy diet promotion.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".