Behind Closed Doors
Bibliographic record
Abstract
The present work studies how community health workers (CHWs) perform the role of educator and how this relates to the implementation of other CHW roles, skills, and qualities. Prior studies on this topic have relied on interviews or focus groups rather than analysis of CHW interactions. We conducted a thematic analysis of 24 transcripts of conversations occurring between CHWs and participants during home visits as part of the Mexican American Trial of Community Health Workers, a randomized controlled trial that improved clinical outcomes among low-income Mexican American adults with type 2 diabetes. Three themes describing interactions related to diabetes self-management education accounted for about half of encounter content. The other half of encounter content was dedicated to interactions not explicitly related to diabetes described by 4 subthemes. In a successful CHW intervention, focused educational content was balanced with other interactions. Interactions not explicitly related to diabetes may have provided space for the implementation of core CHW roles, skills, and qualities other than educator, particularly those related to relationship building. It is important that interventions provide CHWs with sufficient time and flexibility to develop strong relationships with participants.
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 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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.229 | 0.070 |
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".