Exploring the Barriers to and Motivators for Using Digital Mental Health Interventions Among Construction Personnel in Nigeria: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Work-related stress in the construction industry increases the prevalence of depression and anxiety among personnel. In low-resource settings such as Nigeria, construction personnel face high demands and severe working conditions but only have a few services to address their mental health needs. With emerging research showing that digital interventions can be used to self-manage mental health across diverse settings, there may be new opportunities to support construction personnel in the construction industry. OBJECTIVE: This study aims to determine the use of digital interventions for mental health management among construction personnel in Nigeria and to explore the factors that facilitate or impede the use of these interventions. METHODS: This qualitative study explored the perspectives of a convenience sample of 62 construction personnel. The data were subjected to inductive content analysis. RESULTS: A total of 6 barrier and 3 motivator themes were identified and categorized into 2 groups. The barrier themes were subcategorized into barriers to adoption and barriers to persistent use, whereas the motivator themes were subcategorized into intrinsic and extrinsic motivators. Lack of awareness and knowledge about the interventions may constitute a barrier to adoption and use. Participants frequently reported concerns regarding their effectiveness and usability. CONCLUSIONS: This study provides an understanding of the design needs required to facilitate sustained self-management of mental health based on the experiences and expectations of construction personnel with digital interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".