Bibliographic record
Abstract
In primary care settings, primary care physicians (PCPs) commonly have multiple brief visits, often with complex patients and have little time to review the multitude of clinical evidence available that may be relevant to their patients’ needs. This dissertation examines printed educational materials (PEMs) as a tool to facilitate the dissemination of clinical information for PCPs. I use the Technology Acceptance Model to study how the perceived ease of use and perceived usefulness of PEMs influences PCP attitude, intention, and use of PEMs. This dissertation is composed of three studies: 1) A systematic review of 40 studies of PEMs for PCPs to determine the effect of PEMs on patient and physician outcomes, 2) A focus group study to determine PCP preferences for the design and content of PEMs, and 3) The redesign and subsequent usability testing by PCPs of an existing PEM. The results of the systematic review demonstrate that PEMs do not have an effect on patient outcomes or physician cognition or behaviour outcomes. I also conclude that the included studies are limited by very poor intervention reporting. In the second paper, I highlight PCP preferences for PEMs, including that PEMs be relevant to their primary care patient population, short, concise, and specific in terms of their practice recommendations. I also present a list of these preferences for use by individuals who create PEMs. Lastly, in the third paper I redesigned an existing PEM using design principles and user preferences. I conducted a modified discrete choice experiment and usability testing to determine if the redesigned version is more usable and more often selected than the original. The redesigned PEM scored significantly higher on the System Usability Scale and was selected more often than the original version by PCPs across two settings: an opportunistic and a controlled setting. The opportunistic setting was at a large Canadian conference for family physicians in Quebec City, Canada. The controlled setting experiments were conducted as individual sessions at St. Michael’s Hospital in Toronto, Canada or at the participant’s clinic. This dissertation concludes that though PEMs are not found to be effective for changing patient or physician behaviour in their current state, there is an opportunity to optimize their design and potentially increase their effect on both physician and patient outcomes.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".