Exploring Perceived Educational Needs of Primary Care Providers for Online Training and Education in Dementia (Preprint)
Bibliographic record
Abstract
BACKGROUND Currently there are about 5.6 million Canadians living with dementia with an estimate of nearly 1 million people being diagnosed with dementia by 2031. Primary care Providers (PCPs) are crucial to the management and care of dementia patients. OBJECTIVE This study aimed to evaluate the perceived educational needs and preferences of PCPs for training and education in dementia within an online environment. METHODS A prospective, qualitative research study. Participants were primary care providers purposively recruited currently licensed to practice in Ontario that possessed a patient population with dementia, and were willing to attend a focus group session and/or one to one interview. A deductive content/thematic analysis was used to analyze the data. A total of 19 participants took part in the study across four focus groups (n=15) and four individual interviews (n=4). RESULTS The study found that the notion of credibility of information is critical to the learning process and highly valued by physicians. Credibility appears to overlap across the two constructs and the importance of credibility seems to link notions of perceived behaviour control with physicians’ subjective norms. Participants expressed a need for learning that can support their ability to make clinical decisions. Participants expressed the value of educational tools such as technology resources, an online “evidence-based, physician-authored clinical decision support resource and so on. They also wanted to use decision-making tools such as RxFiles. CONCLUSIONS In conclusion, PCPs have educational needs which may pose as facilitators to physician learning, eLearning and continuing medical education.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".