The role of organisational justice, burnout and commitment in the understanding of absenteeism in the Canadian healthcare sector
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to integrate Greenberg's perspective on the connection between injustice and stress in order to clarify the role of organisational justice, burnout and organisational commitment in the understanding of absenteeism. DESIGN/METHODOLOGY/APPROACH: The study was carried out among 457 workers of a large healthcare establishment in the Canadian public healthcare sector. The model was tested using structural equation methods. FINDINGS: The results reveal that procedural and interactional justices have an indirect effect on exhaustion through distributive injustice. Moreover, it was found that distributive injustice is indirectly linked to short-term absences through exhaustion. By contrast, the relationship between distributive injustice and long-term absence can be explained by two mediating variables, namely, exhaustion and psychosomatic complaints. RESEARCH LIMITATIONS/IMPLICATIONS: In spite of the non-longitudinal nature of this study, the results suggest that the stress model and the medical model best explain the relationship between organisational injustice and absenteeism, while the withdrawal model via organisational commitment is not associated in this study with absenteeism. PRACTICAL IMPLICATIONS: Healthcare managers should consider the possibility of better involving employees in the decision-making process in order to increase their perception of procedural and interactional justice, and indirectly reduce exhaustion and absenteeism through a greater perception of distributive justice. SOCIAL IMPLICATIONS: For the healthcare sector, the need to reduce absenteeism is particularly urgent because of budget restrictions and the shortage of labour around the world. ORIGINALITY/VALUE: This is one of the first studies to provide a complete model that analyses the stress process in terms of how organisational justice affects short- and long-term absences, in a bid to understand the specific process and factors that lead to shorter and longer episodes of absence.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".