Value Conflict, Lack of Rewards, and Sense of Community as Psychosocial Risk Factors of Burnout in Communication Professionals (Press, Radio, and Television)
Bibliographic record
Abstract
Journalists are at particular risk of work-related stress and burnout. The objective of this study is to describe and analyze the principal factors involved in the appearance of burnout in communication professionals, as well as the possible interactions between them and with self-reported health, and to observe whether the variables involved are the same in different types of environments. To achieve this objective, 292 participants answered the following measurement instruments: Demographic and labor datasheet; Maslach Burnout Inventory (MBI General survey); Areas of Worklife Scale (AWS); and General Health Questionnaire (GHQ -12). The results were the following: Emotional Exhaustion (EE) shows direct correlation and statistical significance with the other two burnout dimensions, Depersonalization (DP) and Personal Accomplishment (PA), also with health perception variables and inverse and statistical significance with the workload, control, rewards, community, fairness, and values. A multiple linear regression model shows workload and values as inverse EE predictors, which confirms a burnout process in which EE contributes as the main dimension in DP and is shown to be a precursor of PA, itself. When comparing different types of media, journalists who work in institutional press offices presented significantly lower scores in PA and higher in control, rewards, community, justice, and values. Therefore, further research should be carried out in order to analyze the protective role of these variables regarding PA and burnout.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".