The impact of telework on absenteeism, presenteeism, and return to work among workers with health conditions: a scoping review
Bibliographic record
Abstract
Introduction: Telework has become increasingly prominent as a flexible work arrangement, particularly since the COVID-19 pandemic. For workers managing health conditions, it may support continued employment by influencing key work-related phenomena such as absenteeism, presenteeism and return to work (RTW) process. However, current evidence on the impact of telework on the work-related outcome to manage health condition in the workplace remains limited and fragmented. Objective: This scoping review aimed to map the existing literature on the impact of telework on absenteeism, presenteeism, and RTW outcomes among adult workers with health conditions. Methods: Included studies were either qualitative, quantitative, or mixed methods, published in English or French, including adults with any physical or psychological health conditions. At least one outcome domain (absenteeism, presenteeism, or RTW) was required. Eight databases were searched from inception to May 2025: Medline, CINAHL, APA PsycINFO, Academic Search Complete, Business Source Complete, Scopus, Sociological Abstracts, and ABI/INFORM Global. Data extraction focused on study design, objectives, variables/definitions, sample size, health status, demographic characteristics, individual characteristics, organizational factors and results. Data were synthesized by the outcome domain (absenteeism, presenteeism, RTW) and stratified by study type (quantitative vs. qualitative). Results: From 4,093 records, 21 studies were included. The majority of studies suggest that telework contributes to reduced absenteeism by increasing work flexibility. Telework is also consistently associated with facilitating RTW, particularly following surgery or in the context of chronic illness, by supporting work reintegration and shortening the duration of sick leave. In contrast, findings on presenteeism are conflicting: some studies report that telework increases the likelihood of working while sick, others suggest a decrease, and some report no significant impact or conflicting results. These outcomes appear to be influenced by contextual factors, including health status, demographic variables, individual characteristics, and organizational context. Conclusion: Telework appears to offer flexibility that can reduce absenteeism and facilitate RTW. However, its impact on presenteeism is less consistent and may even encourage working while sick if not properly supervised. Future studies should examine which policies most effectively maximize the benefits of telework while minimizing potential drawbacks.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".