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Record W4409332710 · doi:10.1037/pas0001386

Validation of the Moral Injury Outcome Scale in Canadian health care workers.

2025· article· en· W4409332710 on OpenAlexaffabout
Rachel A. Plouffe, Stephanie A. Houle, Michelle Birch, Natalie Ein, Anthony Nazarov, J. Don Richardson

Bibliographic record

VenuePsychological Assessment · 2025
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsLawson Health Research Institute
Fundersnot available
KeywordsPsychologyMoral injuryPsychometricsTest validityScale (ratio)Health careOccupational safety and healthOutcome (game theory)Clinical psychologySocial psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

= 786). The evaluation included its factorial, convergent, discriminant, and concurrent validity, measurement invariance, and internal consistency reliability. Results showed that the MIOS demonstrated a two-factor structure that was invariant at the configural, metric, and scalar level when compared with Canadian Veterans. Lastly, the MIOS showed strong convergent, discriminant, and concurrent validity. Overall, our findings revealed that the MIOS possesses robust psychometric properties. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.141
GPT teacher head0.607
Teacher spread0.465 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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