Assessment of confidence in medical writing: Development and validation of the first trustworthy measurement tool
Bibliographic record
Abstract
BACKGROUND: The popularity of medical writing workshops highlights the need for a standard measurement tool to assess the impact of such workshops on participants' confidence in: 1- writing a standard article and 2- using optimal English language. Because such an instrument is not yet available, we undertook this study to devise and evaluate the first measurement tool to assess such confidence. METHOD: We created an item pool of 50 items by searching Medline, Embase, and Clarivate Analytics to find related articles, using our prior experience, and approaching the key informants. We revised and edited the item pool, and redundant ones were excluded. Finally, the 36-item tool comprised two domains. We tested it in a group of workshop applicants for internal consistency and temporal reliability using Cronbach's α and Pearson correlations and for content and convergent validity using the content validity index and Pearson correlations. RESULTS: The participants had a mean age of 40.3 years, a female predominance (74.3%), and a majority of faculty members (51.4%). The internal consistency showed high reliability (> 0.95). Test-retest reliability showed very high correlations (r = 0.93). The CVI for domain 1 was 0.78, for domain 2 was 0.73, and for the entire instrument was 0.75. CONCLUSION: This unique, reliable, and valid measurement tool could accurately measure the level of confidence in writing a standard medical article and in using the appropriate English language for this purpose.
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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 | 0.072 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".