Organisational justice and mental health: a systematic review of prospective studies: Figure 1
Bibliographic record
Abstract
The models most commonly used, to study the effects of psychosocial work factors on workers' health, are the demand-control-support (DCS) model and Effort-Reward Imbalance (ERI) model. An emerging body of research has identified Organisational Justice as another model that can help to explain deleterious health effects. This review aimed: (1) to identify prospective studies of the associations between organisational justice and mental health in industrialised countries from 1990 to 2010; (2) to evaluate the extent to which organisational justice has an effect on mental health independently of the DCS and ERI models; and (3) to discuss theoretical and empirical overlap and differences with previous models. The studies had to present associations between organisational justice and a mental health outcome, be prospective, and be entirely available in English or in French. Duplicated papers were excluded. Eleven prospective studies were selected for this review. They provide evidence that procedural justice and relational justice are associated with mental health. These associations remained significant even after controlling for the DCS and ERI models. There is a lack of prospective studies on distributive and informational justice. In conclusion, procedural and relational justice can be considered a different and complementary model to the DCS and ERI models. Future studies should evaluate the effect of change in exposure to organisational justice on employees' mental health over time.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".