Community-based Health Data Cooperatives Towards Improving the Immigrant Community Health: A Scoping Review to Inform Policy and Practice
Bibliographic record
Abstract
BACKGROUND: In the case of immigrant health and wellness, data are the key limiting factor, where comprehensive national knowledge on immigrant health and health service utilisation is limited. New data and data silos are an inherent response to the increase in technology in the collection and storage of data. The Health Data Cooperative (HDC) model allows members to contribute, store, and manage their health-related information, and members are the rightful data owners and decision-makers to data sharing (e g. research communities, commercial entities, government bodies). OBJECTIVE: This review attempts to scope the literature on HDC and fulfill the following objectives: 1) identify and describe the type of literature that is available on the HDC model; 2) describe the key themes related to HDCs; and 3) describe the benefits and challenges related to the HDC model. METHODS: We conducted a scoping review using the five-stage framework outlined by Arskey and O'Malley to systematically map literature on HDCs using two search streams: 1) a database and grey literature search; and 2) an internet search. We included all English records that discussed health data cooperative and related key terms. We used a thematic analysis to collate information into comprehensive themes. RESULTS: Through a comprehensive screening process, we found 22 database and grey literature records, and 13 Internet search records. Three major themes that are important to stakeholders include data ownership, data security, and data flow and infrastructure. CONCLUSIONS: The results of this study are an informative first step to the study of the HDC model, or an establishment of a HDC in immigrant communities. KEY WORDS: community health, health data, cooperative, and citizen data empowermen.
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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 | 0.025 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".