An international tool to measure perceived stressors in intensive care units: the PS-ICU scale
Bibliographic record
Abstract
Abstract Background The intensive care unit is increasingly recognized as a stressful environment for healthcare professionals. This context has an impact on the health of these professionals but also on the quality of their personal and professional life. However, there is currently no validated scale to measure specific stressors perceived by healthcare professionals in intensive care. The aim of this study was to construct and validate in three languages a perceived stressors scale more specific to intensive care units (ICU). Results We conducted a three-phase study between 2016 and 2019: (1) identification of stressors based on the verbatim of 165 nurses and physicians from 4 countries (Canada, France, Italy, and Spain). We identified 99 stressors, including those common to most healthcare professions (called generic), as well as stressors more specific to ICU professionals (called specific); (2) item elaboration and selection by a panel of interdisciplinary experts to build a provisional 99-item version of the scale. This version was pre-tested with 70 professionals in the 4 countries and enabled us to select 50 relevant items; (3) test of the validity of the scale in 497 ICU healthcare professionals. Factor analyses identified six dimensions: lack of fit with families and organizational functioning; patient- and family-related emotional load; complex/at risk situations and skill-related issues; workload and human resource management issues; difficulties related to team working; and suboptimal care situations. Correlations of the PS-ICU scale with a generic stressors measure (i.e., the Job Content Questionnaire) tested its convergent validity, while its correlations with the Maslach Burnout Inventory-HSS examined its concurrent validity. We also assessed the test–retest reliability of PS-ICU with intraclass correlation coefficients. Conclusions The perceived stressors in intensive care units (PS-ICU) scale have good psychometric properties in all countries. It includes six broad dimensions covering generic or specific stressors to ICU, and thus, enables the identification of work situations that are likely to generate high levels of stress at the individual and unit levels. For future studies, this tool will enable the implementation of targeted corrective actions on which intervention research can be based. It also enables national and international comparisons of stressors’ impact.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".