Assessing Clinical Faculty Understanding of Statistical Terms Used to Measure Treatment Effects and Their Application to Teaching
Bibliographic record
Abstract
INTRODUCTION: Understanding of statistical terms used to measure treatment effect is important for evidence-informed medical teaching and practice. We explored knowledge of these terms among clinical faculty who instruct and mentor a continuum of medical learners to inform medical faculty learning needs. METHODS: This was a mixed methods study that used a questionnaire to measure a health professional's understanding of measures of treatment effect and a focus group to explore perspectives on learning, applying, and teaching these terms. We analyzed questionnaire data using descriptive statistics and focus group data using thematic analysis. RESULTS: We analyzed responses from clinical faculty who were physicians and completed all sections of the questionnaire (n = 137). Overall, approximately 55% were highly confident in their understanding of statistical terms; self-reported understanding was highest for number needed to treat (77%). Only 26% of respondents correctly responded to all comprehension questions; however, 80% correctly responded to at least one of these questions. There was a significant association among self-reported understanding and ability to correctly calculate terms. A focus group with clinical/medical faculty (n = 4) revealed themes of mentorship, support and resources, and beliefs about the value of statistical literacy. DISCUSSION: We found that half of clinical faculty members are highly confident in their understanding of relative and absolute terms. Despite the limitations of self-assessment data, our study provides some evidence that self-assessment can be reliable. Recognizing that faculty development is not mandatory for clinical faculty in many centers, and the notion that faculty may benefit from mentorship in critical appraisal topics, it may be appropriate to first engage and support influential clinical faculty rather than using a broad strategy to achieve universal statistical literacy. Second, senior leadership in medical education should support continuous learning by providing paid, protected time for faculty to incorporate evidence in their teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".