Predicting burnout based on metacognitive beliefs and alexithymia mediated by optimism in nurses
Bibliographic record
Abstract
Introduction: The commitment of nurses to their jobs and patients has made nursing one of the human service professions susceptible to burnout syndrome. Therefore, the present study aimed to predict burnout based on metacognitive beliefs and alexithymia with the mediation of optimism in nurses. Materials and Methods: The current research was based on applied purpose and descriptive-correlational research design, specifically, structural equation modeling. The statistical population in this research was all the nurses in the hospitals of Qom City in 1401, and 200 nurses were randomly selected to participate in the present research. In this field study, the participants completed Burnout questionnaires by Meslech and Jackson (1986), a metacognition questionnaire by Wells and Cartwright-Houghton (2004), the Toronto dyslexia scale, and the Scheer and Carver optimism scale (1994). Data were analyzed using the Pearson correlation test and structural equation model using SPSS-24 and AMOS-24 software. Results: The results showed that the correlation coefficients between the variables are significant (p<0.01) and there is a positive and significant relationship between metacognitive beliefs and alexithymia with burnout in nurses, and there is a negative and significant relationship between optimism and burnout (p<0.01). Also, there was a negative and significant relationship between metacognitive beliefs and alexithymia and nurses' optimism (p<0.01). Also, the results of the path analysis showed that optimism plays a mediating role in the relationship between metacognitive beliefs and alexithymia with nurses' burnout. Conclusion: In general, current research emphasizes the importance of optimism in the relationship between metacognitive beliefs and alexithymia with nurses' burnout and optimism is an effective factor in reducing nurses' burnout.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".