Challenges and impacts of specialist care wait times identified by family physicians: Results from a cross-sectional survey
Bibliographic record
Abstract
<h3>Context:</h3> Canadians experience longer wait times for specialist referrals compared to other countries, which is a top barrier to health care in Canada and has a negative impact on patient health and quality of life. However, little is known about the impacts on primary care practice. <h3>Objective:</h3> To understand primary care provider (PCP) experiences of long wait times for specialist referrals, and the impacts on PCPs. <h3>Study design and analysis:</h3> A cross-sectional, linked survey was conducted. Descriptive statistics were computed for demographics, and response frequencies were calculated. Open text fields in the survey were thematically analyzed. <h3>Setting:</h3> Surveys were conducted as part of a larger study in Nova Scotia, Canada between 2015 and 2019. Follow-up surveys about specialist wait times were distributed between May and September of 2018. <h3>Population studied:</h3> PCP (i.e., family physician and nurse practitioner) respondents from the larger survey who agreed to participate in this follow-up survey. <h3>Instrument:</h3> Cross-sectional survey tool. <h3>Outcome measures:</h3> How specialist wait times affected primary care practice. <h3>Results:</h3> Of the 566 PCPs invited to take part in the initial survey, 98 (17.3%) agreed to participate in the follow-up survey, and 87 (88.78%) responded to the open-text question. Respondents’ ages ranged from 32 to 72 years, with representation across gender, provider type, practice type, and rurality. Of the 87 respondents, there were 156 responses to the open text question. We identified nine themes: 1) pervasiveness of problematic specialist wait times; 2) managing beyond scope while waiting for specialist care; 3) consequences for patients due to specialist wait times; 4) managing patient expectations while waiting for specialist care; 5) scheduling repeat visits to meet needs of patients; 6) “lost time” to manage patients waiting for specialist access; 7) additional work strategizing ways to access specialist care for patients; 8) provider experience of burnout, frustration and stress due to delayed specialist care; and 9) recommendations for accessing specialist care. <h3>Conclusion:</h3> Long wait times for specialist care in Nova Scotia have negative impacts on both patients and PCPs. Although PCPs took numerous steps to manage their patients in the interim, system-level changes are needed to reduce problematic wait times. These changes could support the health system across the Quintuple Aim.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".