The Health Impact of Social Community Enterprises in Vulnerable Neighborhoods: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: This 4-year research project focuses on 6 social community enterprises (SCEs) that operate in 5 neighborhoods in a Dutch city. Residents of these neighborhoods face problems such as poor average levels of physical and mental health, high unemployment rates, and weak social cohesion. SCEs offer residents social, cultural, and work-related activities and are therefore believed to help these persons develop themselves and strengthen the social ties in the community. Because of a lack of empirical evidence; however, it is unclear whether and how SCEs benefit the health and well-being of participants. OBJECTIVE: This paper outlines a protocol for an evaluation study on the impact of SCEs, aiming to determine (1) to what extent SCEs affect health and well-being of participating residents, (2) what underlying processes and mechanisms can explain such impact, and (3) what assets are available to SCEs and how they can successfully mobilize these assets. METHODS: A mixed methods multiple-case study design including repeated measurements will be conducted. Six SCEs form the cases. An integrated model of SCE health intervention will be used as the theoretical basis. First, the impact of SCEs is measured on the individual and community level, using questionnaires and in-depth interviews conducted with participants. Second, the research focuses on the underlying processes and mechanisms and the organizational and sociopolitical factors that influence the success or failure of these enterprises in affecting the health and well-being of residents. At this organizational level, in-depth interviews are completed with SCE initiators and stakeholders, such as municipal district managers. Finally, structurally documented observations are made on the organizational and sociopolitical context of the SCEs. RESULTS: This research project received funding from the Netherlands Organization for Health Research and Development in 2018. Data collection takes place from 2018 until 2022. Data analysis starts after the last round of data collection in 2022 and finalizes in 2024. Expected results will be published in 2023 and 2024. CONCLUSIONS: Despite the societal relevance of SCEs, little empirical research has been performed on their functioning and impact. This research applies a variety of methods and includes the perspectives of multiple stakeholders aiming to generate new empirical evidence. The results will enable us to describe how SCE activities influence intermediate and long-term health outcomes and how the organizational and sociopolitical context of SCEs may shape opportunities or barriers for health promotion. As the number of these initiatives in the Netherlands is increasing rapidly, this research can benefit many SCEs attempting to become more effective and increase their impact. The findings of this research will be shared directly with relevant stakeholders through local and national meetings and annual reports and disseminated among other researchers through scientific publications. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37966.
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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 | 0.083 | 0.054 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.060 | 0.013 |
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".