Survivorship workshop delivery to primary care providers.
Bibliographic record
Abstract
2 Background: There are approximately 15 million cancer survivors (CS) in North America. In addition to higher risk of recurrence, over 50% of CS are afflicted with late effects. CS are burdened with more medical conditions than the general population. CS visit specialists during treatment, and 75% of them also see their primary care provider (PCP) during and after treatments. Despite their integral role in survivorship care, PCP lack survivorship knowledge and have low confidence regarding CS care, supporting the urgent need to educate them about CS issues and surveillance needs. This study aimed to assess the educational benefit of a survivorship workshop targeting PCP in Montreal, Canada. Methods: An accredited workshop based on NCCN’s 8 common survivor issues was developed and delivered to 155 PCP at 5 sites. Matched pre and post surveys were designed using Likert scale and short-answer questions, and were completed on a voluntary basis by PCP. Specific outcome measures were based on the first 3 levels of Kirkpatrick’s learning model: satisfaction, knowledge, and behaviour. Data analysis included an open-coding approach to identify major themes of qualitative data. Ethics approval was granted. Results: Response rate was 64%. 95% indicated high satisfaction and relevancy of content for primary care. Using t-tests to compare pre and post responses, results were statistically significant for both “list 2 standards of survivorship” and “name 2 late-effects of cancer treatment” survey items, indicating an increase in both standards and late effects identified post workshop. 99% expressed behavioural intent to incorporate survivorship information into practice. Conclusions: Much research has focused on identifying PCP barriers to optimal survivorship care delivery such as limited topic proficiency, yet further efforts are warranted to close that knowledge gap. Our findings revealed increased knowledge of CS issues and surveillance needs post workshop. 3 months survey data is being collected to evaluate for actual practice changes and willingness to manage specific CS issues (i.e. treating depression). A second workshop targeting cardiovascular health promotion interventions has been created: delivery is anticipated early 2016.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".