Perception of mothers and male partners of the use of an electronic health data recording system while attending reproductive and child health clinics in Kilimanjaro, Tanzania: A qualitative study
Bibliographic record
Abstract
Abstract Introduction : Electronic health record systems (EHRSs) can promote maternal and perinatal healthcare through timely data capture, data accuracy, continuity of care, and service efficiency. It is unclear how clients to whom the data are collected perceive the process or understand its use. This study investigated the perception of the use of EHRSs during the perinatal period among mothers and male partners attending reproductive and child health (RCH) clinics in the Kilimanjaro Region. Methods : A qualitative study was conducted between April and June 2024 via focus group discussions and in-depth interviews within reproductive and child health clinics around the communities of the Moshi District Council (rural) and the Moshi Municipal Council (urban). The research involved mothers who gave birth in the past 12 months before this study and male escorting partners attending RCH clinics.The data were collected via Sem-structured interview guides and analyzed thematically via NVivo 15 software. Data triangulation, reflexivity, and adherence to qualitative research standards were observed, ensuring that the study met ethical and methodological rigor. To address these components, our study established a trustworthy and robust system. Results : A total of 39 participants were recruited for the study, i.e., 29 mothers who gave birth between March 2023 and March 2024 and 10 male partners attending RCH clinics. The findings revealed that most participants had positive opinions of EHRS due to its efficacy and safety, ability to reduce access time, ensure continuity of care, increase trust through confidentiality, increase data storage, and simplify work. However, limited client awareness, privacy concerns, infrastructure problems, and financial limitations are some of the obstacles that might affect its adoption. Systems integrating healthcare facilities with training for clients and healthcare providers and creating policy documents to ensure sustainability and efficacy were proposed. Conclusion : Our study reflects a representative range in the transition of EHRSs. Although some concerns around understanding, privacy issues, infrastructure, and technical capabilities were revealed, the positive outcomes indicate significant opportunities in improving quality-of-care delivery through the EHRS. Resolving these challenges is crucial for smoother transitions and building trust in EHRSs over time.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".