A Comparison of Census and Cohort Sampling Models for the Longitudinal Collection of User-Reported Data in the Maternity Care Pathway: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Typical measures of maternity performance remain focused on the technical elements of birth, especially pathological elements, with insufficient measurement of nontechnical measures and those collected pre- and postpartum. New technologies allow for patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) to be collected from large samples at multiple time points, which can be considered alongside existing administrative sources; however, such models are not widely implemented or evaluated. Since 2018, a longitudinal, personalized, and integrated user-reported data collection process for the maternal care pathway has been used in Tuscany, Italy. This model has been through two methodological iterations. OBJECTIVE: The aim of this study was to compare and contrast two sampling models of longitudinal user-reported data for the maternity care pathway, exploring factors influencing participation, cost, and suitability of the models for different stakeholders. METHODS: Data were collected by two modes: (1) "cohort" recruitment at the birth hospital of a predetermined sample size and (2) continuous, ongoing "census" recruitment of women at the first midwife appointment. Surveys were used to collect experiential and outcome data related to existing services. Women were included who passed 12 months after initial enrollment, meaning that they either received the surveys issued after that interval or dropped out in the intervening period. Data were collected from women in Tuscany, Italy, between September 2018 and July 2020. The total sample included 7784 individuals with 38,656 observations. The two models of longitudinal collection of user-reported data were analyzed using descriptive statistics, survival analysis, cost comparison, and a qualitative review. RESULTS: Cohort sampling provided lower initial participation than census sampling, although very high subsequent response rates (87%) were obtained 1 year after enrollment. Census sampling had higher initial participation, but greater dropout (up to 45% at 1 year). Both models showed high response rates for online surveys. There were nonproportional dropout hazards over time. There were higher rates of dropout for women with foreign nationality (hazard ratio [HR] 1.88, P<.001), and lower rates of dropout for those who had a higher level of education (HR 0.77 and 0.61 for women completing high school and college, respectively; P<.001), were employed (HR 0.87, P=.01), in a relationship (HR 0.84, P=.04), and with previous pregnancies (HR 0.86, P=.002). The census model was initially more expensive, albeit with lower repeat costs and could become cheaper if repeated more than six times. CONCLUSIONS: The digital collection of user-reported data enables high response rates to targeted surveys in the maternity care pathway. The point at which pregnant women or mothers are recruited is relevant for response rates and sample bias. The census model of continuous enrollment and real-time data availability offers a wider set of potential benefits, but at an initially higher cost and with the requirement for more substantial data translation and managerial capacity to make use of such data.
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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 | 0.211 | 0.264 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".