Effects of a telehealth programme using mobile data transmission on primary healthcare utilisation among children in Bamako, Mali
Bibliographic record
Abstract
Pesinet is a non-profit organisation which operates a microinsurance programme combined with a monitoring service in low-income countries to increase primary healthcare utilisation for children. We studied the association between enrolment in the Pesinet programme and changes in utilisation of health services. We conducted a prospective controlled study in Bamako (Mali) in children under five years old. Participants in the Pesinet service were recruited from a neighbourhood of Bamako (n = 91) and participants in the control group (usual care) came from two other neighbouring districts (n = 89). Eight questionnaires were completed at 2-week intervals for each child in the study. We performed logistic regression modelling to assess the effect of the Pesinet programme on health service utilisation, adjusting for confounding variables (age and socio-economic status). During the study, families reported 206 episodes of disease in the intervention group and 168 in the control group. Children from the intervention group had 85 medical consultations and those in the control group had 28. Based on the logistic regression model, there was increased utilisation of health care services among children enrolled in the Pesinet programme, with an adjusted Odds Ratio for medical consultations of 2.2. Membership of the Pesinet telehealth programme increased primary healthcare utilisation among children under five years old in Mali.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".