Developing an mHealth Intervention to Reduce COVID-19–Associated Psychological Distress Among Health Care Workers in Nigeria: Protocol for a Design and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Globally, COVID-19-related psychological distress is seriously eroding health care workers' mental health and well-being, especially in low-income countries like Nigeria. The use of mobile health (mHealth) interventions is now increasingly recognized as an innovative approach that may improve mental health and well-being. This project aims to develop an mHealth psychological intervention (mPsyI) to reduce COVID-19-related psychological distress among health care workers in Nigeria. OBJECTIVE: Our objective is to present a study protocol to determine the level of COVID-19-related psychological distress among health care workers in Nigeria; explore health care workers' experience of COVID-19-related psychological distress; develop and pilot test mPsyI to reduce this distress; and assess the feasibility of this intervention (such as usability, engagement, and satisfaction). METHODS: A mixed (quantitative and qualitative) methods approach is used in which health care workers will be recruited from 2 tertiary health care facilities in southwest Nigeria. The study is divided into 4 phases based on the study objectives. Phase 1 involves a quantitative survey to assess the type and levels of psychosocial distress. Phase 2 collects qualitative data on psychosocial distress among health care workers. Phase 3 involves development of the mHealth-based psychological intervention, and phase 4 is a mixed methods study to assess the feasibility and acceptability of the intervention. RESULTS: This study was funded in November 2020 by the Global Effort on COVID-19 Health Research, and collection of preliminary baseline data started in July 2021. CONCLUSIONS: This is the first study to report the development of an mHealth-based intervention to reduce COVID-19-related psychological distress among health care workers in Nigeria. Using a mixed methods design in this study can potentially facilitate the adaptation of an evidence-based treatment method that is culturally sensitive and cost-effective for the management of COVID-19-related psychological distress among health care workers in Nigeria. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36174.
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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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".