Quality Improvement: An Assessment of Participation and Attitudes of Medical Oncologists
Bibliographic record
Abstract
PURPOSE: Although quality improvement (QI) is an integral part of cancer care, there are few QI publications in the medical oncology literature. We examined the prevailing attitudes of medical oncologists toward QI and causes for the low QI publication rate in the medical oncology literature. METHODS: Using a modified Dillman method, we distributed a 13-question online survey to medical oncologists across Canada asking about their attitudes toward and involvement in QI and perceived barriers to publishing QI studies. RESULTS: We attained a 43% response rate (143 of 332). Of the responding oncologists, 97% (138) agreed that QI was an important aspect of their practice, although only 49% (70) had participated in QI in the past 5 years. Physicians with administrative responsibility were more likely than clinicians to be involved in QI (P = .008). Most QI participants focused on domains of safety (70%) and patient centeredness (67%). Among QI participants, 72% did not publish their findings, because of lack of time (34%), no identifiable journals (14%), and unfamiliarity with QI methodology (10%). Barriers for QI nonparticipants included uncertainty about how to get involved (45%), lack of time (18%), and limited institutional support or recognition (18%). QI participants had greater awareness of recent practice-changing QI publications compared with nonparticipants (P = .003). CONCLUSION: Canadian medical oncologists face limitations to participating in and publishing QI initiatives because of lack of knowledge about ongoing initiatives, lack of time, and lack of resources to aid publication. Improving networking opportunities and prioritizing QI at the institutional level can address this need.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".