A survey of physicians' views on the Saskatchewan cancer agency's follow-by-mail program
Bibliographic record
Abstract
Canadian cancer clinics are straining to keep up with growing numbers of patients and, as a result, the capacity to provide follow-up care to cancer patients is being stretched. The Saskatchewan Cancer Agency has structured its community follow-up program to ensure the routine follow-up of patients who have finished active cancer treatments. Follow-up letters are routinely sent to family physicians and some specialists requesting information on the disease status of their cancer patients. For this thesis, I conducted a mail survey of 925 Saskatchewan physicians serving 21,000 patients to learn about general practitioners' and specialists' views of the follow-up program. A 52.5% response rate was achieved. The program was considered useful for 91.5% of physicians, with the follow-up letter serving an important role in reminding physicians to see their cancer patients for follow-up. High percentages of physicians indicated a need for additional patient-specific information (59.3%), clinical information (73.0%) and training (34.9%) to do follow-up. Logistic regression analyses found female gender, a specialty in general practice and lower physician confidence in following cancer to be associated with the need for additional patient information. Lower physician confidence was associated with the need for additional clinical information and a specialty in general practice and lower physician confidence were associated with the need for more training. Percentages of physicians saying they were very confident in following various cancers ranged widely from 19.1% for lymphomas to 54.2% for breast cancer. All regression models regarding physician confidence in following six different cancers had a common correlate: the need for additional training. A physician's number of follow-up patients was a significant correlate in four of the six regression models and physician specialty was included in half of the models. The results suggest areas of the program and physician need that should be addressed to ensure the delivery of quality follow-up care and the survey findings will be helpful in devising strategies to this end. At the same time, responses indicate the program to be an essential component in the delivery of community-based follow-up care in Saskatchewan.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".