Patterns of referral and knowledge of palliative radiotherapy in Alberta.
Bibliographic record
Abstract
OBJECTIVE: To assess practitioners' referral patterns and knowledge of palliative radiotherapy (PRT). DESIGN: A 23-item questionnaire. SETTING: Northern Alberta and parts of British Columbia, Saskatchewan, the Northwest Territories, and Nunavut. PARTICIPANTS: A total of 1360 health practitioners, including primary care physicians and nurse clinicians in rural, remote, or far northern regions; FP-oncologists working in community cancer centres; palliative care (PC)specialists; and medical oncologists. MAIN OUTCOME MEASURES: Survey respondents rated how much certain factors influenced their decisions to refer patients for PRT and estimated their knowledge of PRT. Descriptive and summary statistics were compiled. RESULTS: The overall eligible response rate was 31.8% (412 of 1294); 85.4% of respondents were FPs, 65.3% were men, and 44.9% practised in rural settings. A total of 81.8% of respondents sometimes or often provided PC and 71.0% had referred patients for PRT. Main factors taken into account when referring patients were functional status (93.1%; 349 of 375), histology (75.4%; 285 of 378), and concern about side effects (75.3%; 281 of 373).Half of respondents considered wait times for PRT delivery important. Self-rated knowledge of PRT was poor for 74.0% of respondents, fair for 24.5%, and good for 1.5%. Actual knowledge scores were poor for 46.6% of respondents, fair for 36.7%, and good for 16.7%. Respondents who referred patients for PRT had been in practice longer, saw more cancer patients per month, provided PC more frequently, had higher self-rated PRT knowledge,and had better actual PRT knowledge. CONCLUSION: Disease- and patient-related factors outweighed concerns about wait times. Although referring practitioners are better informed than they believe themselves to be, further improvements in their knowledge could increase referrals of appropriate patients for PRT.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".