Identifying validity evidence for uncertainty tolerance scales: A systematic review
Bibliographic record
Abstract
PURPOSE: Uncertainty tolerance (UT) is increasingly valued as a medical graduate attribute and broadly measured among medical student populations. However, the validity evidence underpinning UT scale implementation has not been summarised across studies. The present work evaluates UT scale validity evidence to better inform when, why and how UT scales ought to be used and to identify remaining validity evidence gaps. METHODS: A literature search for psychometric studies of UT scales was completed in 2022. Records were included if they implemented one of the four most cited UT scales (i.e. Physicians' Reactions to Uncertainty scale 1990 [PRU1990] or 1995 [PRU1995], Tolerance for Ambiguity [TFA] scale or Tolerance of Ambiguity in Medical Students and Doctors scale [TAMSAD]) in a population of physicians and/or medial students and presented validity evidence according to the Standards for Educational and Psychological Testing framework. Included studies were rated and analysed according to evidence for test content, response processes, internal structure, relations to other variables and consequences of testing. RESULTS: Among the investigated scales, 'relations to other variables' and 'internal structure' were the most commonly reported forms of validity evidence. No evidence of 'response processes' or 'consequences of testing' was identified. Overall, the PRU1990 and PRU1995 demonstrated the strongest validity evidence, although evidence primarily related to physician populations. CONCLUSIONS: None of the studied scales demonstrated evidence for all five sources of validity. Future research would benefit from assessing validity evidence for 'response processes' and 'consequences of testing' among physicians and medical students at different training/career stages to better understand UT construct conceptualisation in these populations. Until further and stronger validity evidence for UT scales is established, we caution against implementing UT scales outside of research settings (e.g. for higher stakes decision making).
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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 | 0.073 | 0.358 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".