Health Worker Training Materials ~ Smart Discharges
Bibliographic record
Abstract
<strong>Objective(s):</strong> The Smart Discharges Health Worker Training Program uses a train-the-trainer model to improve the quality of discharge care. Core learning components include: 1) understanding why children die during the vulnerable period after discharge; 2) conducting risk assessment for post-discharge vulnerability (facility-based health workers only); and 3) Effective counselling practices. <br /><strong>Data Description:</strong> This dataset includes the following materials for use in the Smart Discharges Training Program: 1) Facilitators Guide; 2) Health Workers Guide; 3) Community Health Worker’s Trainer’s Manual; 4) Smart Discharges Training Pre-Post Test; 5) Smart Discharges Training Program Evaluation Form. Materials were originally developed by WALIMU, in partnership with the University of British Columbia, and were adapted by the Uganda Ministry of Health. <br />All materials are provided in the English language. <br /><strong>Limitations:</strong> These materials were designed for the Ugandan context and may not be generalizable to other settings. <br /><strong>Abbreviations:</strong> Village Health Teams (VHT) (i.e. local term for Community Health Worker (CHW)) <br /><strong>Ethics Declaration:</strong> NA <br /><strong>Funding Source(s):</strong> BC Children's Hospital Foundation; Grand Challenges Canada; Mining4Life; Thrasher Research Fund;
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".