VALIDATION OF PRIMARY CARE QUESTIONNAIRES ON KNOWLEDGE, ATTITUDES, AND PRACTICES TOWARD DEMENTIA
Bibliographic record
Abstract
In 2013, the Quebec Ministry of Health implemented a reform called the ‘Alzheimer Plan’ in 42 family medicine groups (FMG) to better manage dementia patients in primary care. We created and validated two questionnaires to measure the clinicians’ knowledge, attitudes, and practices regarding dementia care in each FMG. Based on the literature and content validation with experts, one 72-item questionnaire for family physicians and another 70-item questionnaire for nurses and other health professionals were created. The questionnaires were distributed to 865 clinicians after the beginning of the AD plan. Exploratory factor analysis (EFA) was performed to examine the construct validation of each questionnaire and determine the underlying subscales. Prior to the EFA, a clinically-relevant structure of subscales was elaborated. Five subscales were identified within the physicians’ questionnaire: 1) perceived competency and knowledge, 2) attitudes toward the AD Plan, 3) practices for cognitive evaluation, 4) attitudes toward the disease, and 5) collaboration with nurses and other professionals. Four subscales were identified within nurses and other professionals’ questionnaire: 1) perceived competency and knowledge, 2) attitudes toward the AD Plan, 3) attitudes toward the dementia patients and their care-givers, and 4) perceived support from external resources. These subscales were consistent with the a priori clinically-derived structure. Items within the subscales correlated well together and differentiated well across subscales.
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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 | 0.036 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 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".