Effects of an Internet Delivered Behavioral Activation Program on Improving Work Engagement Among Japanese Workers
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to examine the effect of a newly developed Internet-delivered behavioral activation (iBA) program on work engagement and well-being among Japanese workers with elevated psychological distress. METHODS: Participants were recruited via an Internet survey company ( N = 3299). The eligibility criteria were as follows: (1) Japanese employees aged 20 to 59 years, (2) having psychological distress, and (3) not self-employed. This iBA program was a 3-week web-based training course using behavioral activation techniques. Work engagement, psychological distress, and eudemonic well-being at work were measured at baseline and postintervention period. A paired sample t test was conducted to assess the intervention effect. RESULTS: Of the 568 eligible participants, 120 were randomly selected. A total of 108 participants completed the baseline survey and received the iBA program. Eighty respondents completed the postintervention survey and were included in analyses. The iBA program did not show a significant intervention effect on work engagement ( P = 0.22, Cohen d = 0.14), while psychological distress ( P < 0.01, d = -0.40) and role-oriented future prospects ( P = 0.02, Cohen d = 0.27) were significantly improved. CONCLUSIONS: The effect of the iBA program on work engagement may be limited.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".