The predictors of absenteeism due to psychological disability: A longitudinal study in the education sector
Bibliographic record
Abstract
BACKGROUND: Being absent from work because of a psychological disability is costly for both individuals and organizations and frequent in employees working in the field of education. Absenteeism from work has been mostly studied as an organizational withdrawal behavior related to negative factors. OBJECTIVE: The purpose of this longitudinal study is to define the predictors of absenteeism due to psychological disability by taking into account resources, such as Self-determined work motivation and Subjective well-being, as well as symptoms of Psychological distress. PARTICIPANTS: The sample consisted of 261 employees from a Canadian public school organization. METHODS: Independent sample t-tests were conducted to compare the mean scores of participants who were not absent from work and participants who were absent due to psychological disability. Logistic regression analyses were computed for the dependent variable to assess the contribution of the three independent variables. RESULTS: Participants who were absent from work due to psychological disability in the year following the data collection scored significantly lower on resources, and higher on symptoms than those participants who were not absent. The three-predictor model was found to be significant. However, only Self-determined work motivation and Psychological distress significantly predicted absenteeism due to psychological disability. CONCLUSIONS: Results are discussed in terms of psychological processes regulating the relationships between the work-related factors (i.e., work motivation) and life-related factors (i.e., psychological distress and subjective well-being) of personal adjustment and accomplishment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".