Reducing Accessibility Issues at Primary Care Clinics in Canada through Design Thinking: Introducing a Digital Self-scheduling Booking Application
Bibliographic record
Abstract
Health care services must be universally accessible and timely for the well-being of Canadians. Although Canadians have free access to medically necessary services at the point of entry, significant barriers exist, particularly in primary care clinics. These include lengthy wait times, language obstacles for newcomers, and inadequate information about available clinics and doctors. Such challenges can make it difficult for individuals, especially more vulnerable populations like immigrants and students, to navigate the system effectively. Waiting for a primary health care appointment can create both physical and emotional stress, especially for those experiencing pain or anxiety about a potential health condition. Delays in receiving care may lead to deterioration in one’s condition or increased worry. Furthermore, the current method for booking appointments—primarily via a phone call—has several hidden drawbacks. Many patients report long hold times, difficulties in communication due to language barriers, and a perception of judgment or unhelpful behaviour from receptionists, which can particularly affect immigrants and students. Given these issues, this thesis aims to improve accessibility to general health and walk-in clinics in Canada by addressing key challenges such as long clinic wait times, limitations in appointment booking systems, and a general lack of transparent information about doctors and clinic reviews. Research conducted to address these problems included user interviews, literature reviews, user surveys, and competitor analysis. The proposed designed response was to develop a self-scheduling digital booking platform with a human-centred design approach. This platform would allow patients to book appointments online, see up-to-date wait times, and access reliable information about doctors and clinics, empowering users to make informed choices while improving the overall experience of accessing health care services.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".