Assessing courtesy reporting bias in facility-based surveys on person-centred maternity care: evidence from urban informal settlements in Nairobi and Lusaka
Bibliographic record
Abstract
Background: Experience of care is typically measured through client exit surveys administered in the facility. Evidence suggests that such measures suffer from courtesy reporting bias whereby respondents do not accurately report on their experiences while in the facility. We explored the presence of courtesy bias by comparing women's reported experience of person-centred maternity care (PCMC) from facility-based client exit surveys to mobile phone-based surveys out of the facility in Nairobi and Lusaka's urban informal settlements. Methods: We randomly and independently sampled women in the facilities for either a facility-based survey (n = 233 in Lusaka and n = 112 in Nairobi) or a mobile phone-based survey (n = 203 in Lusaka and n = 300 in Nairobi) within one to two weeks of facility discharge. The questionnaire included a validated PCMC scale. After adjusting for differences in women's characteristics across groups, we compared PCMC scores between facility and phone-based samples. We ran multilevel linear regression models to assess PCMC by survey modality in each city. Results: In both cities, over 70.0% of women were aged 20-34 years and were married, at least two thirds had secondary education, and over 95.0% were unaccompanied during labour/delivery. The overall PCMC score was 69.3% among women surveyed on the phone compared to 70.2% among those surveyed in the facility in Nairobi. In Lusaka, it was 57.5% on the phone compared to 56.8% in-facility. We found no statistically significant differences in PCMC scores between survey modalities in both cities, after adjusting for differences in women's characteristics. Conclusions: We did not detect significant courtesy reporting bias in PCMC in facility-based client exit surveys in the context of urban informal settlements in Nairobi and Lusaka. Experience of PCMC can be measured through in-facility client exit surveys or mobile phone surveys. However, it is critical to address challenges related to a mobile phone-based approach.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".