An Evaluation of Health Numeracy among Radiation Therapists and Dosimetrists
Bibliographic record
Abstract
Purpose Medical errors in radiation oncology sometimes involve tasks reliant on practitioners' grasp of numeracy. Numeracy has been shown to be suboptimal across various health care professionals. Herein, we assess health numeracy among American Society of Radiologic Technologists (ASRT) members. Methods and materials The Numeracy Understanding for Medicine instrument (NUMi), an instrument to measure numeracy in the general population, was adapted to oncology for this study and distributed to ASRT members (n = 14,228) in 2017. Per NUMi scoring, health numeracy scores were categorized as low (0-7), low average (8-12), high average (13-17), or high (18-20). The impact of cGy versus Gy on numeracy performance was investigated. Spearman's rho and a Wilcox-Mann-Whitney test were used for comparisons between the different groups. Results A total of 662 eligible participants completed the instrument and identified as radiation oncology professionals. In the cGy and Gy NUMi scores, approximately 2% of respondents scored low-average, approximately 40% scored high-average, and approximately 58% scored high, with a median score of 18.0. Although the optimum NUMi score for ASRT members is unknown, one might expect our cohort to have numeracy skills at least as high as college freshmen. Roughly one-sixth of our study group scored at or below the average score of college freshmen (NUMi = 15). In the subset analysis of NUMi questions pertaining to radiation dose unit (cGy vs Gy), respondents performed better with cGy (mean score: 2.94; range, 2-3) versus Gy (mean: 2.91; range, 0-3; P = .011). Conclusions In this study of limited sample size, overall numeracy is quite good compared with the general population. However, the range of scores is wide, and some respondents have lower scores that may be concerning, suggesting that numeracy may be an issue that requires improvement for a subset of the studied cohort. Performance was superior with the unit cGy; thus, the adoption of cGy as the standard unit is reasonable.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".