Specialist LINK and primary care network clinical pathways - a new approach to patient referral: a cross-sectional survey of awareness, utilization and usability among family physicians in Calgary
Bibliographic record
Abstract
BACKGROUND: Specialist LINK is a real-time, non-urgent telephone collaboration line designed to link family doctors and specialists. The purpose was to reduce wait times, improve efficiency and enhance the coordination of patient care through enhanced communication between primary and specialty care. The aim of this study was to determine the awareness and utilization of Specialist LINK and Primary Care Network (PCN) Clinical Pathways among family physicians. METHODS: A family physician experience cross-sectional survey was conducted from March to May 2018 in Calgary and Area. The survey was designed to assess family physicians' awareness and utilization of Specialist LINK and PCN Clinical Pathways. We also used a 1-10 scale for respondents to rate the utility of Specialist LINK (1 was least useful and 10 represented highly useful). To obtain a true representative sample, family physicians were selected through a random sampling method. We applied multiple approaches to ensure a high response rate: paper survey, telephone reminders, and an on-site survey for non-responders. RESULTS: A total of 251 participants completed the survey of the 650 randomly selected family physicians (Response rate≈39%). Eighty-nine percent of the family physicians were aware of Specialist LINK [95% Confidence Interval (84-92%)]. The average rating was 8.1 (on a scale of 1-10) for the usefulness of Specialist LINK. We found that the odds of being aware of Specialist LINK were two times higher in female family physicians compared to male physicians. Also, those with less than 5 years of experience, the odds of being aware of Specialist LINK were around five times higher compared to those with 5 or more years of experience. Fifty-five percent of family physicians were aware of PCN Clinical Pathways (95% CI = 48-60%); of those, 82% were accessing and following PCN Clinical Pathways in their clinical practice. The average rating was 7.9 (on a scale of 1-10) for the usefulness of PCN Clinical Pathways. CONCLUSION: Most of the respondents in Calgary and area were aware of Specialist LINK and a large proportion of them were using it to access advice for their patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".