A community-driven communicative approach to adoption of a client record management system
Bibliographic record
Abstract
A lesson to be learned from research in information and communication technology for development (ICT4D) is that the best information management system will fail if it is not embraced and competently employed by end users in all stages of workflow, especially those end users with little prior familiarity with computer and database use. Because patient record management systems rely heavily on input from lay practitioners and other less trained staff, they are particularly vulnerable to failure related to end user non-acceptance. This paper presents a case study of a client database system introduced to novice users in a department of social work of a developing country. Factors impacting user acceptance were perceived system utility, congruence with present work practices and cultural appropriateness. We describe an educational intervention that was observed to improve end users' attitudes toward introduction of the system as well as their ability to integrate it into their work processes. While still in its evaluation phase, we believe this description of the requirements elicitation process and educational intervention represents an important step in the integration and use of patient record management systems in developing communities.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".