Employee Preference and Use of Employee Mental Health Programs: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Mental health issues represent a prevalent challenge for employees and their employers, leading to substantial impacts on individuals, society, and the economy. Different employee mental health programs (EMHPs) can support employees in preventing and treating mental health issues. However, the impact of such EMHPs depends largely on the use behavior of employees. OBJECTIVE: This study aimed to gain deeper insights into employees' attitude and use behavior regarding EMHPs by investigating (1) employee preference and intention to use EMHPs, (2) factors that predict use, and (3) key facilitators and barriers influencing use. METHODS: An exploratory sequential mixed methods approach was applied, including a scoping review, qualitative interviews, and a quantitative web-based survey. Semistructured qualitative interviews were conducted with 15 employees to gain insights into EMHPs from the employee perspective and inform the creation of a web-based questionnaire. The quantitative web-based survey was conducted to collect representative primary data on employees' perspectives on different EMHPs using 7-point Likert scales. The collected quantitative data were analyzed through descriptive and inferential statistics, including repeated measures ANOVAs and chi-square tests. RESULTS: The final sample of the web-based survey consisted of 1134 participants and was representative across several sociodemographic characteristics. Analysis of the sample revealed that when given the choice, employees preferred digital (n=666, 58.73%) and self-intervention (n=590, 52.03%) EMHPs. Employees were most likely to use EMHPs focused on prevention (mean 4.89, SD 1.61). Intention to use EMHPs was predicted by age (young: mean 4.59, SD 1.2; old: mean 4.19, SD 1.4; P<.001; Cohen d=0.32), education (academic degree: mean 4.68, SD 1.24; no academic degree: mean 4.26, SD 1.32; P<.001; Cohen d=0.32), and mostly by company culture (positive company culture: mean 4.61, SD 1.27; negative company culture: mean 3.99, SD 1.27; P<.001; Cohen d=0.49). Cost coverage (n=345, 30.42%) and ease of use (n=337, 29.72%) were critical facilitators of use. CONCLUSIONS: Employers can have a positive contribution to employee mental health by starting to offer EMHPs, preferably digital self-intervention programs for prevention; creating and maintaining the right work environment and culture; and ensuring cost coverage for the EMHP.
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".