Primary care physicians' attitudes towards cognitive screening: findings from a national postal survey
Bibliographic record
Abstract
Abstract Objective The objectives of this paper are: (a) to determine Canadian family physicians' attitudes towards cognitive screening, (b) to identify what cognitive screening tools are being used, (c) to investigate how they rate these tools' effectiveness and (d) to identify the attributes of an ideal cognitive screening tool for the primary care setting. Method Postal survey questionnaire of a random sample of 249 practicing members of the College of Family Physicians of Canada. Results Response rate was 52%. The majority of physicians ‘Agreed’ or ‘Strongly Agreed’ that cognitive impairment assessment is important in primary care (89%), and ‘Disagreed’ or ‘Strongly Disagreed’ that it should be left to specialists (92%). However, 35% were undecided when asked if assessment in primary care would lead to better outcomes. The most frequently used assessment tools were Mini–Mental Status Exam (MMSE), Clock Drawing, Delayed Word Recall, Standardized MMSE and Alternating Sequences, but were mainly rated as only ‘Good’ in terms of perceived effectiveness. Validity/accuracy was identified as the top attribute of an ideal screening tool. Female physicians were more likely to have a positive attitude towards cognitive assessment. Younger physicians, those in group practices, or those with either ≤ 20% or 61–80% of elderly patients in their practice indicated a shorter ideal time to administer a cognitive screening tool. Conclusion Despite general agreement that primary care physicians have an important role in cognitive screening, there is less agreement that it leads to better outcomes. The development of a superior screening tool to be used in the primary care setting is needed. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".