Teaching Performance Mediated by Emotional Well-Being: A Systematic Review
Bibliographic record
Abstract
Teaching professionals continually face challenges that affect their work performance. One of these challenges is related to emotional well-being, which is influenced by the different work demands, institutional changes, the teaching-learning activity itself, and the interaction with students. Considering the impact on teacher performance, assessing and considering their emotional well-being is necessary. This article aimed to analyze how teaching performance is mediated by emotional well-being. For this purpose, a systematic literature review was carried out, the search process of which was supported by the PRISMA methodology. The search process was carried out in the Scopus, WoS, and SciELO databases, from which a total of 24 articles were obtained that met the established inclusion criteria. It is concluded that for effective work performance, teachers must enjoy emotional well-being; in addition, educational policies must be oriented to ensure the emotional health of teachers, making appropriate interventions that focus on the different negative and positive emotions that these professionals may experience in their daily practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".