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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".