Patient experiences using primary care wait lists in Canada: A qualitative study
Bibliographic record
Abstract
Context: Primary healthcare is the front door to the health system, facilitating access to diagnostic testing and specialist care. Patients without a regular primary care provider (a family physician or nurse practitioner) are considered “unattached,”. In 2019, 14.5% of Canadians (approximately 4.6 million people aged 12 and older) were unattached to a regular primary care provider. To address ongoing challenges with patient access and attachment to a primary care provider, several Canadian provinces introduced primary healthcare centralized wait lists (CWL). Objective: Our study explores patient experiences using these CWLs in three Canadian provinces. Study design and analysis: A qualitative study using the perceived organizational justice lens as a theoretical underpinning. Relevant data, pertaining specifically to patients, were analyzed thematically. Setting: Three Canadian provinces Ontario (ON), Quebec (QC), and Nova Scotia (NS). Population studied: Unattached patients in ON, QC, and NS. Instrument: Semi-structured exploratory qualitative interviews. Outcome measures: Perspectives and experiences from unattached patients on the utility of the CWL. Results: We interviewed 41 patients across the three provinces. Four themes were identified during the analysis regarding the utility of CWL: 1) CWL communication, 2) CWL registration, 3) interactions with the CWL, and 4) experiences of attachment and “failed” attachment via the CWL. Comparisons between CWLs within the three provinces were made. Conclusion: Participants heard about the CWL through formal and informal mechanisms and recommended improving communication about CWLs and how they work. Participants registered for the CWL when they relocated, when their provider left their practice, or when the need for primary care was urgent. Participants shared numerous challenges registering on the CWL and felt that greater communication is needed between the CWL and those enrolled. Many participants wished for greater transparency about their status on the list. Participants experienced both successful and unsuccessful attachment through the CWL, although many participants found that wait times were lengthy and wished for transparency about CWL wait times.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".