Psychological stress measure (PSM-9): Integration of an evidence-based approach to assessment, monitoring, and evaluation of stress in physical therapy practice
Bibliographic record
Abstract
Stress can be a primary or secondary contributor to ill health via excessive and sustained sympathetic arousal leading to ischemic heart disease, hypertension, stroke, obesity, and mental ill health, or through related behaviors such as smoking, substance abuse, and over or inappropriate eating; or as a contextual variable in terms of risk factor and lifestyle outcome. In addition, psychosocial stress can impair recovery from any pathological insult or injury. Most assessments of stress relate to life events, and both past and current life stressors, acute and chronic, play a major role. However, beyond the impact of stressors, it is the reported state of feeling stressed that is the critical predictor of ill health. This article describes stress and its correlates, discusses models of stress, and presents the nine-item Psychological Stress Measure (PSM-9). This tool is aimed directly at the state of feeling stressed, is suited for assessing stress clinically in the general population and serving as an outcome measure. The tool is valid and reliable and easy to administer in health care settings; it has a normal distribution, which makes it a very sensitive-to-change instrument in repeated measures to document progress.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".