Mixed-methods evaluation of acceptability of the District Health Information Software (DHIS2) for neglected tropical diseases program data in Cameroon
Bibliographic record
Abstract
Background The District Health Information Software (DHIS2), adopted as national health information system by the ministry of public health in Cameroon, did not integrate neglected tropical diseases (NTD) program data. Integrating NTD program data into the national DHIS2 might require more than technical skills. Our study aimed to explore the factors that affect acceptability and use of DHIS2 by NTD stakeholders for successful integration of NTD program data into the national DHIS2. Methods For purposes of this mixed-methods study, the data were collected through a self-administered questionnaire targeting NTD stakeholders at different levels of the health pyramid from all the ten Regions in Cameroon. The questionnaire was based on a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model, supplemented by a qualitative analysis to assess the acceptability, and use of the DHIS2 as a platform for NTD program data in Cameroon. Results We found 81.9% (95% confidence interval, CI=0.784-0.859; standard error=0.019) of intention to use DHIS2 for NTDs program data and 18.4% (95% CI=0.130-0.289; standard error=0.041) of actual use among survey participants. Social influence (β=0.269, P=0.000), voluntariness of use (β=0.243, P=0.000), performance expectancy (β=0.186, P=0.010), and training adequacy (β=0.199, P=0.000) would positively influence intention to use DHIS2. Computer anxiety (β=-0.230, P=0.000) and technology experience (β=0.374, P=0.000) would have a significant negative and positive effect on actual use, respectively. The most critical challenges in using DHIS2 referred to facilitating conditions (conditions of the work environment), specifically electricity and internet connection, impeding actual use of DHIS2. Conclusions Our study revealed that NTD stakeholders in Cameroon are ready to accept DHIS2 for NTD program in Cameroon. However, to ensure its successful implementation. For example, we recommend that NTD program managers plan adequate support in providing proper training, non-vendor specific 2G-3G-4G internet modems with data bundle and smartphones/laptops to ease the use of DHIS2 by NTD stakeholders. We showed that acceptability of DHIS2 studied through UTAUT model should be complemented with a qualitative analysis for richer insights.
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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 | 0.075 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".