Parallel Surveys of Specialists and Family Physicians in Nova Scotia Regarding Satisfaction With the Referral Process
Bibliographic record
Abstract
Background: The referral letter is fundamental to effective communication between family physicians (FPs) andspecialists. However, this document is frequently cited as a source of frustration for both referring and consultantphysicians. Methods: Aspects of the referral process were identified and assembled in 2 surveys: one distributed to FPs and oneto specialists. The survey used a 5-point Likert scale for each aspect of the referral process. Surveys were mailed to500 family physicians and 500 specialists. Results: There was a 42.4% and 43.8% response rate from FPs and specialists, respectively. Few of the survey itemsstood out as being particularly polarizing. There was no significant difference between FPs and specialists in thelevel of satisfaction, nor among the groups of specialists. Among FPs, those practicing outside of the Capital DistrictHealth Authority (CDHA) had significantly higher overall satisfaction than those practicing within the CDHA. Conclusion: The results of the survey indicate that the referral letter could be improved by focusing on specificaspects of the letter. These points can be targeted at all levels of medical education (undergraduate, postgraduate,and continuing medical education) to help improve a process that is fundamental to our healthcare system. Thedisparity in overall satisfaction between FPs in CDHA and FPs in the rest of the province is intriguing given that CDHAhas the greatest number of specialists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".