A TWO PROCESS MODEL OF BURNOUT: THEIR RELEVANCE TO SPANISH AND CANADIAN NURSES
Bibliographic record
Abstract
Nurses from Spain (N = 834) and Canada (N = 725) completed surveys assessing burnout and their perceptions of worklife. The study explored a two-process model of burnout. First, work overload exhausts nurses by exerting excessive demands and interfering with their capacity to recover energy. Second, enduring conflicts of personal and organizational values have a diverse relationship with burnout. A series of multiple regression analyses examined the relative contributions of these two processes. One process was evident in the contribution of workload to predicting exhaustion that in turn predicted cynicism that predicted efficacy. In parallel, value congruence contributed significantly to the regressions on each of the three aspects of burnout in addition to the workload-exhaustion-cynicism-efficacy process. Further, multiple regression analyses demonstrated that other areas of worklife-control, reward, community, and fairness-were strongly associated with value congruence in a manner distinct from the relationship of values with manageable workload. The two samples showed evidence of both processes, but that the workload/exhaustion process was dominant for the Canadian sample while the values/burnout process was more relevant for the Spanish sample. Implications for a comprehensive model of burnout are discussed.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".