Staff, space, and time as dimensions of organizational slack
Bibliographic record
Abstract
BACKGROUND: : In the theoretical and research literature, organizational slack has been largely described in terms of financial resources and its impact on organizational outcomes. However, empirical research is limited by unclear definitions and lack of standardized measures. PURPOSE: : The aim of this study was to assess the psychometric properties of a new organizational slack measure in health care settings. METHODS: : A total of 752 nurses and 197 allied health care professionals (AHCPs) employed in seven pediatric Canadian hospitals completed the Alberta Context Tool, an instrument measuring organizational context, which includes the newly developed organizational slack measure. The nine-item, 5-point Likert organizational slack measure includes items assessing staff perceptions of available human resources (staffing), time, and space. We report psychometric assessments, bivariate analyses, and data aggregation indices for the measure. FINDINGS: : The findings indicate that the measure has three subscales (staff, space, and time) with acceptable internal consistency reliability (alphas for staff, space, and time, respectively:.83,.63, and.74 for nurses;.81,.52, and.76 for AHCPs), links theory and hypotheses (construct validity), and is related to other relevant variables. Within-group reliability measures indicate stronger agreement among nurses than AHCPs, more reliable aggregation results in all three subscales at the unit versus facility level, and higher explained variance and validity of aggregated scores at the unit level. PRACTICE IMPLICATIONS: : The proposed organizational slack measure assesses modifiable organizational factors in hospitals and has the potential to explain variance in important health care system outcomes. Further assessments of the psychometric properties of the organizational slack measure in acute and long-term care facilities are underway.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".