Recruiting and Surveying Catholic Parishes for Cancer Control Initiatives
Bibliographic record
Abstract
BACKGROUND: We describe activities undertaken to conduct organizational surveys among faith-based organizations in Massachusetts as part of a larger study designed to promote parish-based cancer control programs for Latinos. METHOD: Catholic parishes located in Massachusetts that provided Spanish-language mass were eligible for study participation. Parishes were identified through diocesan records and online directories. Prior to parish recruitment, we implemented a variety of activities to gain support from Catholic leaders at the diocesan level. We then recruited individual parishes to complete a four-part organizational survey, which assessed (A) parish leadership, (B) financial resources, (C) involvement in Hispanic Ministry, and (D) health and social service offerings. Our goal was to administer each survey component to a parish representatives who could best provide an organizational perspective on the content of each component (e.g., A = pastors, B = business managers, C = Hispanic Ministry leaders, and D = parish nurse or health ministry leader). Here, we present descriptive statistics on recruitment and survey administration processes. RESULTS: Seventy-five percent of eligible parishes responded to the survey and of these, 92% completed all four components. Completed four-part surveys required an average of 16.6 contact attempts. There were an average of 2.1 respondents per site. Pastoral staff were the most frequent respondents (79%), but they also required the most contact attempts (M = 9.3, range = 1-27). While most interviews were completed by phone (71%), one quarter were completed during in-person site visits. CONCLUSIONS: We achieved a high survey completion rate among organizational representatives. Our lessons learned may inform efforts to engage and survey faith-based organizations for public health efforts.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".