Pool of items to measure Primary Health Care workers’ knowledge on healthy eating
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a self-applicable instrument for measuring primary health care (PHC) workers' knowledge on healthy eating. METHODS: A six-step methodological study to develop and validate a measurement instrument: item development based on the Brazilian Dietary Guidelines' chapters; content validation with a panel of experts; face validation with potential instrument users; online instrument reevaluation by participants of the content and face validation panels; online application of the instrument with PHC workers; confirmatory factor analysis for construct validation. RESULTS: A first version with 25 items underwent content and semantic changes in the content and face validation panels, being reorganized into a second version with 22 items. In the reevaluation, participants considered 21 questions to be clear and representative of the Brazilian Dietary Guidelines, with one being excluded. This third version of the instrument underwent confirmatory factor analysis after being applied online with 209 PHC workers from all Brazilian macroregions. We excluded five items in this analysis: four due to bivariate empty cells and one due to low discrimination capacity. The final model, with 16 items loaded onto one dimension, returned good fit indices [χ2(104) = 119.047, p = 0.1486; RMSEA = 0.026 (90% CI = 0.000 to 0.046), Cfit = 0.979; CFI = 0.924; TLI = 0.913]; its information peak was below average. CONCLUSIONS: The instrument proved to be valid and accurate for assessing PHC workers with below average knowledge of the Brazilian Dietary Guidelines. It might contribute to improving actions to promote healthy eating in Brazilian PHC settings by identifying the need for training health professionals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".