Success of health cell approach in improving knowledge, attitude and practice related to COVID-19: difference-in-differences analyses of a community-based quasi-experimental trial
Bibliographic record
Abstract
OBJECTIVES: There remain hesitations and miscommunication regarding appropriate public health behaviours and conceptions related to COVID-19. We tested the effectiveness of the community-based health cell approach in improving knowledge, attitude and practice (KAP) related to COVID-19. SETTING: Households of the Bauniabadh slum area in Mirpur, Dhaka, Bangladesh. PARTICIPANTS: Household heads (HHs) and homemakers (HMs) of intervention (n=211) and comparison households (n=209). INTERVENTIONS: Behaviour change communication delivered at the community level in a quasi-experimental manner through small-scale community meetings and home visits. OUTCOME VARIABLES AND METHODS: The outcomes of interest were before-after mean and per cent changes in KAP scores. Data were collected from HHs and HMs before and after the intervention and difference-in-differences (DID) analysis technique was applied. RESULTS: We found statistically significant (p<0.05) before-after differences in the responses to the KAP questions made by the intervention groups. The DID models estimated the improvements in COVID-19-related KAP of HHs by 16.58 (95% CI: 14.05, 19.12), 20.92 (95% CI: 18.17, 23.67) and 28.45 (95% CI: 23.84, 33.07) per cent points, respectively. The DID estimates of KAP in HMs were 17.8 (95% CI: 15.09, 20.51), 22.33 (95% CI: 19.47, 25.19) and 28.06 (95% CI: 23.18, 32.93) per cent points, respectively. Overall, 20.91 (95% CI: 18.87, 22.94) and 21.81 (95% CI: 19.68, 23.94) per cent points of improvement were observed among HHs and HMs, respectively. The DID estimates of before-after mean changes in different KAP domains ranged from 2.24 to 2.68 units and the overall changes in KAP scores among HHs and HMs were 7.11 (95% CI: 6.42, 7.8) and 7.42 (95% CI: 6.69, 8.14) units. CONCLUSION: Scientifically valid information disseminated at the community level using the health cell approach could bring positive changes in KAP related to COVID-19.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".