Axe the Fax: What Users Think of Electronic Referral
Bibliographic record
Abstract
Long wait times for elective services are seen as one of the major challenges for Canadian healthcare. Canadians report that they wait longer for specialists than citizens in other countries. The main reason for this is that the referral process is poorly coordinated and leads to delays in care. Electronic referral (eReferral) is seen as a potential means of improving the referral process and enabling faster access to care. There is the potential for national implementation of eReferral in Canada to help achieve this aim. However, existing initiatives have encountered challenges with user adoption and users have continued to use fax. A validated tool was used to survey both users of fax as well as users of eReferral. These two groups of users were then compared. Most family physicians using fax were satisfied overall with the process. This highlighted how challenging any change of this engrained technology will be. There were, however, some significant areas were eReferral was superior to fax. This included response time, the overall quality of referral information, completeness of the information, the timeliness of the information, and the format and layout. There is an opportunity to leverage these findings to support the adoption of eReferral and help reduce 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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".