Educating primary care providers about cancer survivorship.
Bibliographic record
Abstract
20 Background: Nearly 50% of cancer survivors (CS) experience psychosocial and physical treatment-related effects. CS are often afflicted with greater medical conditions than non-cancer patients: survivorship care is thus imperative. In addition to follow-up by specialists, 75% of CS also visit their primary care provider (PCP) during and after treatments. Despite their role in survivorship, insufficient knowledge and low confidence have been reported by PCPs, supporting the need to educate them. This study aimed to evaluate the educational benefit of a survivorship workshop (SW) targeting PCPs in Montreal, Canada. Methods: An accredited 60-minute SW based on common survivorship issues and recommended guidelines by recognized entities was developed and delivered to 167 PCPs at 6 sites. The same MD presented each SW. Brief matched pre, post and 3 month delayed post surveys were designed (Likert-scale and short-answer questions), and completed on a voluntary basis. Outcome measures targeted 3 levels of Kirkpatrick’s learning model: satisfaction, knowledge, and behavior. Data were analyzed with parametric (paired t-tests) and non-parametric (Wilcoxon Signed rank tests) comparisons as appropriate. Results: The pre and post survey response rate was 65.3% and the 3-month delayed post survey response rate was 56.9%. Immediately following the SW, participants were significantly more likely to be able to list standards of survivorship, t(108) = 10.50, p < .001, and to name late-effects of cancer treatment, t(108) = 5.52 , p < .001. High relevance and satisfaction of SW was reported (95%), and 99% expressed intent to incorporate survivorship information into practice. At 3 months post-SW, confidence remained significantly higher than pre-intervention levels for both knowledge of “late physical effects” ( Z = 6.08, p < .001, n = 60) and “adverse psychosocial outcomes” of cancer and treatments (Z = 4.26, p < .001, n = 62). Conclusions: Much literature has focused on determining PCP barriers to survivorship care, including limited topic proficiency, yet further initiatives are warranted to optimize PCP survivorship expertise. Our SW increased PCP survivorship knowledge, and confidence levels remained greater at 3 months post, indicating its educational merit.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".