Use of Social Media for Cancer Prevention Through Neighborhood Social Cohesion: Protocol for a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Social cohesion is associated with healthier behaviors and better health outcomes, and therefore may offer a mechanism for promoting better health. Low socioeconomic status (SES) communities face higher rates of chronic disease due to both community- and individual-level factors. OBJECTIVE: The aim of this study is to leverage social cohesion to promote healthier behaviors and prevent chronic disease in a low SES community. This protocol outlines the methodology for a pilot study to assess the feasibility of an intervention (Free Time For Wellness [FT4W]) using a social networking platform (Nextdoor) with mothers living in an urban, low-income community to improve social cohesion and promote healthy behaviors. METHODS: The study will involve three phases: (I) co-designing the intervention with mothers in the neighborhoods of interest, (II) implementing the intervention with community leaders through the social networking platform, and (III) evaluating the intervention's feasibility. Phase I of the study will include qualitative data collection and analysis from in-depth, semistructured interviews and a co-design group session with mothers. Phases II and III of the study include a pre- and postintervention survey of participating mothers. Neighborhood-level data on social cohesion will also be collected to enable comparison of outcomes between neighborhoods with higher and lower baseline social cohesion. RESULTS: As of March 2021, recruitment and data collection for this study are complete. This protocol outlines our original study plan, although the final enrollment numbers and intervention implementation deviated from our initial planned methodology that is outlined in this protocol. These implementation learnings will be shared in subsequent publications of our study results. CONCLUSIONS: Ultimately, this study aims to: (1) determine the barriers and facilitators to finding free time for wellness among a population of low-income mothers to inform the co-design process, and (2) implement and study the feasibility of an intervention that leverages social cohesion to promote physical activity in a community of low-income mothers. The results of this study will provide preliminary feasibility evidence to inform a larger effectiveness trial, and will further our understanding of how social cohesion might influence well-being. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/28147.
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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 | 0.048 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.088 | 0.022 |
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".