Anticipated Acceptability of Blended Learning Among Lay Health Care Workers in Malawi: Qualitative Analysis Guided by the Technology Acceptance Model
Bibliographic record
Abstract
Background: HIV index case testing (ICT) aims to identify people living with HIV and their contacts, engage them in HIV testing services, and link them to care. ICT implementation has faced challenges in Malawi due to limited counseling capacity among lay health care workers (HCWs). Enhancing capacity through centralized face-to-face training is logistically complex and expensive. A decentralized blended learning approach to HCW capacity-building, combining synchronous face-to-face and asynchronous digital modalities, may be an acceptable way to address this challenge. Objective: The objective of this analysis is to describe factors influencing HCW anticipated acceptability of blended learning using the Technology Acceptance Model (TAM). Methods: This formative qualitative study involved conducting 26 in-depth interviews with HCWs involved in the ICT program across 14 facilities in Machinga and Balaka, Malawi (November-December 2021). Results were analyzed thematically using TAM. Themes were grouped into factors affecting the 2 sets of TAM constructs: perceived usefulness and perceived ease of use. Results: A total of 2 factors influenced perceived usefulness. First, HCWs found the idea of self-guided digital learning appealing, as they believed it would allow for reinforcement, which would facilitate competence. They also articulated the need for opportunities to practice and receive feedback through face-to-face interactions in order to apply the digital components. In total, 5 factors influenced perceived ease of use. First, HCWs expressed a need for orientation to the digital technology given limited digital literacy. Second, they requested accessibility of devices provided by their employer, as many lacked personal devices. Third, they wished for adequate communication surrounding their training schedules, especially if they were going to be asynchronous. Fourth, they wished for support for logistical arrangements to avoid work interruptions. Finally, they wanted monetary compensation to motivate learning, a practice comparable with offsite trainings. Conclusions: A decentralized blended learning approach may be an acceptable method of enhancing ICT knowledge and skills among lay HCWs in Malawi, although a broad range of external factors need to be considered. Our next step is to integrate these findings into a blended learning package and examine perceived acceptability of the package in the context of a cluster randomized controlled trial.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".