Measures of spirituality for use in military contexts: a scoping review
Bibliographic record
Abstract
Introduction: The purpose of this scoping review is to identify measures of spiritual fitness that can be used in a military context. Methods: Two independent reviewers applied post-hoc inclusion and exclusion criteria for the identification of relevant articles, reviews, and assessments. Each reviewer independently recorded criteria met using a jointly developed form, considering relevant spiritual fitness screening and assessment tools based on clinical experience. When two reviewers were in disagreement, a third blinded reviewer was used to create consensus. Results: 35 assessments with psychometric properties were identified that could be administered by military chaplains, health care professionals, or military personnel (through self-reporting) to evaluate spiritual fitness over time among those in the military service. The assessments were compiled into a table to isolate differing properties of each assessment, including target population, length/time to complete, measurement of affective, behavioural, and cognitive aspects of spiritual fitness, psychometric properties, and example questions. Discussion: There is now an opportunity to further identify and evaluate spiritual fitness screening and assessment tools that will appropriately and effectively determine the spiritual fitness and resilience of individuals serving in the military as well as their families.
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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 | 0.034 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".