Acceptance of an Internet-Based Team Development Tool Aimed at Improving Work-Related Well-being in Nurses: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Workplace health interventions can produce beneficial health- and business-related outcomes. However, such interventions have traditionally focused on lifestyle behaviors of individuals, mostly not considering the role of working conditions. The wecoach intervention is an internet-based tool that combines both a digital and a participatory team development approach aimed at addressing critical job demands and resources as key aspects of health-promoting working conditions. Nursing staff are particularly affected by challenging working conditions and could potentially benefit greatly. Understanding the acceptance of novel workplace health promotion approaches is a critical precursor to their successful implementation and use. OBJECTIVE: This study aims to examine the factors influencing the acceptance of a digitally supported team development tool among nurse managers. METHODS: A sample of 32 nurse managers from 3 German-speaking countries tested wecoach and completed our online questionnaire. Hypotheses were based on the unified theory of acceptance and use of technology (UTAUT) and the organizational health development (OHD) model and were tested using multiple regression analyses. RESULTS: Our analyses found that merely capacities on the team level (CapTeam) significantly contributed to the acceptance of wecoach, although only after the other variables were excluded in the stepwise multiple regression analysis. The UTAUT predictors were unable to add significant variance explanation beyond that, and their inclusion masked the contribution of CapTeam. CONCLUSIONS: For the acceptance of a digitally supported participatory tool, the fit with the team, its culture, and its motivation are of critical importance, while aspects proposed by traditional acceptance models, such as the UTAUT, may not be applicable.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".