Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine whether single-item measures of job stressor facets were as valid as multiple-item measures in predicting psychological strain. Single-item measures are more time and cost efficient than multiple-item measures and may also have psychometric benefits. Design/methodology/approach – Data from 3,166 hospital employees were used to evaluate the validity of 11 single-item job stressor facet measures by applying five criteria for content and criterion validity. Findings – Based on this data, six single-item measures of job stressors met all criteria, supporting their use as single-item facet measures. Research limitations/implications – The use of a sample of employees from one female-dominated industry may limit the generalizability of the results to other industries. Future research should replicate the results of the current study in other industries and use longitudinal designs to examine the predictive validity of the single-item measures. Future studies may also develop single-item measures of each facet a priori and examine their validity. Practical implications – Results support the use of single-item measures for the assessment of significance, recognition, workload, work-family conflict, skill use, and coworker relations, which can be included in research where a shorter survey is necessary. These six measures may facilitate more frequent assessment of job stressors, the assessment of job stressors as control variables, and the assessment of multiple job stressors simultaneously, while still minimizing survey space and cost. Originality/value – This is the first study to examine the validity of single-item measures of job stressors, which is a construct that is frequently assessed in organizations.
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 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".