The health-performance framework of presenteeism: A proof-of-concept study
Bibliographic record
Abstract
There is emerging research that considers presenteeism as a neutral behavior that has both positive and negative predictors and outcomes for individuals and organizations. This neutral perspective diverges from the traditional negative view of presenteeism and is aligned with the Health-Performance Framework of Presenteeism (HFPF) in which presenteeism is considered to be an adaptive behavior that aims to balance health limitations and performance demands. This proof-of-concept study aims to investigate the existence of different profiles of presentees based on their common health problems (mental and physical) and performance, and differences in attendance and job stressors among these subgroups. Latent profile analysis with 159 clerical employees and managers from the UK private sector supported the HPFP and revealed four profiles: those reporting a good health and high performance were labeledfunctional presentees(who represented 19% of the sample), those with poor health and low performance were thedysfunctional presentees(14%), those with relatively high performance but poor health were labeledoverachieving presentees(22%), and those with average scores on both dimensions were theaverage Joe/Jane presentees(45%; a new profile based on this sample). There was no profile in the present sample that corresponded totherapeutic presenteeism, characterized by low performance but relatively good health. Althoughaverage Joe/Jane presenteeswere comparable tofunctional presenteesin exposure to most job stressors, they reported poorer pay and benefits, and more health problems than the latter.Average Joe/Jane presenteesreported the lowest number of days of presenteeism. No difference was found in absenteeism across profiles, highlighting difficulties in measuring presenteeism using a count-measure, since three profiles presented a similar number of days of presenteeism yet contrasted health-performance configurations.Dysfunctional presenteeswere systematically more exposed to job stressors compared tofunctional presentees. The results support the HPFP proposition for different subgroups of presentees who are influenced by their work environment. The study takes a person-centered approach, disentangle presenteeism from the total count of presenteeism days, offering implications for management and intervention practice. Presenteeism can have a bright side and be functional in certain contexts when the appropriate resources are available.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".