Psychological capital and well-being: An opportunity for teachers' well-being? Scoping review of the scientific literature in psychology and educational sciences
Bibliographic record
Abstract
In a context marked by teachers' shortage, prioritizing teachers' well-being emerges as one of the factors that can encourage them to stay in the profession. Well-being is a multidimensional concept and difficult to define and measure. Moreover, its link with Psychological Capital (PsyCap), a concept that includes personal psychological resources (hope, self-efficacy, resilience, and optimism) (Luthans & Youssef, 2004), has received little attention in the educational sciences. The main objective of this paper is therefore to investigate the links between these two concepts from a theoretical point of view in psychology and educational sciences. For this purpose, the scoping review methodology (Tricco et al., 2018) is mobilized to identify research issues, methodological questions, and the various links between well-being and PsyCap. Based on a systematic review of 376 bibliographic references conducted in the main databases in psychology and educational sciences, 32 articles were selected analysed. The data extracted indicate that these concepts particularly affect teachers (42.11 % of subjects concerned, N = 42,750). In addition, all the sources selected report significant and positive statistical links between well-being and PsyCap. These results suggest possible avenues for research on teachers' well-being.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".