A MILD DEMENTIA KNOWLEDGE TRANSFER PROGRAM TO IMPROVE KNOWLEDGE AND CONFIDENCE IN PRIMARY CARE
Bibliographic record
Abstract
To the Editor: Early detection of mild dementia facilitates optimal care, which provides social, economic, and medical benefits.1, 2 People with dementia are often unaware of their deficits, so to avoid underrecognition and diagnostic delays,3 primary care professionals must shift to an “active detection” approach, overcoming barriers including unfamiliarity with early indicators, diagnostic uncertainty,4 poor understanding of assessment tools,5 and inexperience.6 A Mild Dementia Knowledge Transfer (MDKT) pilot program incorporating contemporary knowledge translation concepts, practice opportunities with feedback,7 and collaboration between specialty and primary care,8 was developed to increase the knowledge and confidence of primary care professionals in addressing mild dementia. Community primary care professionals (medical and nursing) were recruited within the referral area of the Queen's University memory clinics, ensuring a wide range of baseline knowledge and confidence. The MDKT Program detailed in the figure was conducted at each site (Figure 1). At a baseline meeting, program procedures and patient selection (with or without suspected cognitive deficits, but not diagnosed with dementia) were discussed. Recipients completed the pre-program questionnaire (Q1) and received the MDKT toolkit—Canadian Dementia Guidelines, a Data Gathering Form (DGF)—developed for the program, and the Montreal Cognitive Assessment (MoCA).9 The semistructured DGF informally assesses memory, calculation, abstract thinking, and language. In their own clinics, recipients received training in the use of these tools by assessing their selected patients with supervision on the assessment date. Within 14 days, recipients met with the dementia specialist and assistant to discuss the assessment findings, diagnosis, and initial care planning and then completed a postprogram questionnaire (Q2). Three months afterward, recipients completed a final questionnaire (Q3). Mild Dementia Knowledge Transfer program outline. The questionnaires developed to evaluate this program asked recipients to rate their knowledge and confidence in the detection, assessment, and care of mild dementia on 7-point ordinal scales. Knowledge, confidence, and knowledge plus confidence scores were the primary efficacy outcomes. Open and closed-ended questions asked about recipients' impressions of the program, practice change, and specialist collaboration. Change between the three time points in knowledge, confidence, and knowledge plus confidence scores was tested using linear mixed-effects model analysis with time as fixed effect and intercept as random effect accounting for baseline within-recipient variation. Main effects and interaction effects with time of explanatory variables (previous training, percentage of seniors in roster, program exposure) were tested using a conservative P≤.01. Thirty-eight primary care professionals (23 medical, 15 nursing) from five practice models in 14 sites participated in the study (26 female, 24.2±13.9 years of practice, 24 with previous dementia training, 50.3% seniors in roster). Forty-nine patients were assessed (median 2, range 1–9/recipient), with program exposure determined according to clinics' needs and availability, allowing the program to fit within usual care processes. Recipients showed significantly better scores after the program (Q2) than before (Q1) (all P<.001). Improvements were stable with no decline from Q2 to Q3 (all P>.05), suggesting that the program was effective and potentially created practice change. Improvements were greater in recipients without previous training (P=.01), with fewer elderly patients (P=.003), and who completed more assessments (P=.01), adding support to the effectiveness of the program. Recipients' impressions of the program were very good. Recipients felt that the program increased their knowledge (n=37) and confidence (n=36) in mild dementia and that the program information would be integrated into their practice (n=33), making it easier to detect mild dementia (n=35). Recipients rated the program as excellent (n=27) and effective (n=20), with interactivity (n=25), level of information (n=21), and relevance to job (n=20) as the best parts of the program. Time limitation (n=26) was a perceived barrier for the integration of program information into practice. Several important features of the MDKT program were novel aspects of dementia knowledge translation, which recipients rated highly and were probably important to its success. First, the program was performance oriented, with recipients actively participating in the process of dementia detection, assessment, diagnosis, and care planning, with their own patients providing a context-relevant learning experience; interactive techniques including performance opportunities tend to be more successful.7 Second, the program was specialist supported, allowing sharing of experience and tacit knowledge essential in dementia care, for which active detection is often based on subtle symptoms, diagnosis is clinically integrative, and management is complex. Third, the program was clinic based, being conducted in recipients' own practices, with their own patients greatly enhancing relevance.10 Finally, as reflected in the wide range of program exposures, the program's dynamic and interactive approach was flexible, meeting the needs and time availability of a heterogeneous group of primary care professionals. Conflict of Interest: AG has been a consultant and received honoraria and funds from all major pharmaceutical companies involved in the treatment of dementia (e.g., Pfizer, Janssen-Ortho, Novartis, and Lundbeck). She is also a Board Member of the Canadian Dementia Knowledge Transfer Network, a Canadian Institutes of Health Research–funded research organization. The program was supported by Queen's University and Pfizer Canada. Author Contributions: T. Chesney and A. Garcia developed all components of the Program. B. Alvarado contributed the analysis and interpretation of data. All authors prepared and reviewed the manuscript. Sponsor's Role: Support for the project was unlimited and arm's length. Sponsors had no role in the design, methods, subject recruitment, data collection, analysis, or preparation of the paper. They did not review the letter.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".