Age as a Predictor of Burnout in Russian Public Librarians
Bibliographic record
Abstract
Objective – Increasing life expectancy leads to an increase in the mean age of the workforce. The aging workforce implies new challenges for management and human resources. Existing findings on relations between age and burnout are controversial and scarce. Also, the problem of burnout amongst library workers in Russia has received little attention from researchers. Methods – The studied sample consisted of 620 public librarians from 166 public libraries of different regions (the Moscow region, Yaroslavl, Chelyabinsk, Novosibirsk, Astrakhan, and Republic of Buryatia) of the Russian Federation, who completed a self-reported online survey. For measuring burnout, a new Burnout Assessment Tool was implemented. To examine the associations of interest, we used structural equation modeling with a group correction approach. In addition, library location, general self-efficacy, and length of employment at the current workplace were utilized as predictors. All statistical analysis was performed in R. Results – Findings confirmed the hypotheses partially and revealed negative links between exhaustion, mental distance, and cognitive control and age, while reduced emotional control did not relate to age. Urban librarians tended to demonstrate higher levels of mental distance and had more significant problems with emotional regulation than their rural counterparts. Also, the non-Moscow region librarians did not demonstrate correlations between age and reduced cognitive control. Moreover, they showed a positive link between age and reduced emotional control. Conclusion – The current paper confirmed some previous results on the negative relations between burnout symptoms and chronological age. The results suggest the existence of higher risks of burnout for younger library workers. Potential mechanisms underlying the resilience of older workers 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".