Facilitators and barriers in the use of the electronic consultation register for Integrated Management of Childhood Illness in the health district of Toma, Burkina Faso: Perspectives of health care providers
Bibliographic record
Abstract
Abstract Background In collaboration with the Ministry of Health and Public Hygiene (MHPH) of Burkina Faso (BF), the Foundation Terre des Hommes (Tdh) has developed the Integrated e-Diagnostic Approach (IeDA) project in BF since 2010 to strengthen the health system by digitalizing medical protocols, improving the quality of services and using data. We sought to identify and analyze the barriers and facilitators of using the electronic clinic registry (ECR) for the integrated management of childhood illness (IMCI) by healthcare providers (HCPs) in the health district of Toma, BF. Methods We conducted a descriptive and exploratory qualitative study. In-depth individual interviews were conducted with thirty-five (35) HCPs in the health district of Toma, BF, from the 1 st to the 30 th of December 2021. Thematic analysis of qualitative data according to the Braun & Clarke model was performed using NVivo 12 software and arranged along a social-ecological model. Results Our findings revealed that HCPs play an essential role in using ECR for IMCI. Many key facilitating factors have emerged regarding the use of IMCIs in primary health care (PHC) facilities, such as positive perceptions of the ECR, firm commitment and the involvement of HCPs, stakeholder support, collaborative networks with implementing partners, convenience, privacy, confidentiality and client trust, experience and confidence in using the system, and the satisfaction, motivation and competency of staff. In addition, the easy diagnosis offered by the ECR and the training of HCPs increased the acceptance and use of the ECR. Regarding barriers, HCPs complained about the tablet’s slowness, recurrent breakdowns, and increased workload. Conclusion This study revealed that ECR has excellent potential to improve the quality of care and, in turn, reduce maternal and infant mortality. Although the satisfaction of the HCPs with the tool is positive, the actors of the Foundation Tdh, in collaboration with the MHPH, must work to optimize the application’s performance and reduce breakdowns and delays during consultations. This will allow the deployment of ECR in all BF health districts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".