Multidisciplinary education and action to foster equitable cancer care.
Bibliographic record
Abstract
193 Background: Disparities in cancer care occur when patients are offered inequitable quality of care due to socioeconomic characteristics, affecting patient outcomes (e.g., survival rates). A five-part education (three articles, a webinar, and an infographic) was designed for multidisciplinary oncology care team members to build an enhanced awareness of cancer care disparities and inspire action to foster equitable cancer care. This study evaluated the impact of the education on the learning outcomes of participants. Methods: The evaluation combined quantitative and qualitative methods assessing learners’ knowledge, confidence, change in practice and barriers to change. Collected data included 1) matched pre- and post-activity questions, 2) a post-activity survey evaluation, 3) a post-activity 30-minute interview. Where relevant, categorical data were recoded into binomial values (e.g., 0=incorrect, 1=correct) and knowledge-based responses computed into scores (0-100%). Quantitative data was subject to descriptive and pre-post analysis (McNemar statistical tests for binomial values, paired t-tests for continuous values). Qualitative data (Interview transcripts, open-field responses) were subject to inductive thematic analysis. Results: Of 2,850 completers, 1,151 were graduated healthcare professionals (58% nurses, 23% advanced practice providers, 11% physicians, 8% other), involved in cancer care in the United States. Across all activities, knowledge scores for cancer care disparities and strategies to foster equitable care were significantly higher post-activity (79-91%, depending on activity) than pre-activity (23-62%, p<.001). Similarly, the percentage of learners that were confident in their ability to a) address the personal factors impacting a patient’s willingness to pursue screening or treatment and b) identify practices that can foster equitable care to all cancer patients were significantly higher post-activity (51-75%) than pre-activity (27-38%, p<.001). Thematic analysis of qualitative responses showed that learners identified the following strategies to ensure equitable cancer care: a) multidisciplinary collaboration, b) open lines of communication with patients, c) frequent follow-ups, and d) community outreach. Barriers to change included a lack of organizational capacity and shortage of staff. Conclusions: This intervention encouraged multiple professions in oncology care to learn and reflect on factors driving disparities in cancer care. The intervention was shown to be impactful on the educational outcomes of healthcare professionals in terms of identifying strategies that can tangibly enhance cancer care equity and resulting patient outcomes. A sensitisation and problem-solving approach during the five activities underscored the importance of a multidisciplinary collaboration as a key strategy to addressing cancer disparities.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".