2015 Doctors of the World International Network Observatory: 10 year review of key challenges and lessons learned on health data, methodology, monitoring and reporting
Bibliographic record
Abstract
Background Doctors of the World International Network Observatory has conducted in our free clinics multicenter medical & social surveys (2006-2016) across Europe & Canada with vulnerable people (EU nationals & migrants). It aims to describe health states, social determinants of health & barriers to care in order to inform health policy makers & obtain positive changes. Reflecting on 10 years of this humanitarian health initiative with very limited resources, we identified numerous data collection and methodological challenges, several innovative solutions, and three key lessons learned. Materials and Methods In 2006 & 2008 we run specific surveys only with undocumented migrants (patient n = 835 and 1,218 respectively). In 2011, we changed to routine data concerning all patients seen. In 2015, our Observatory included over 35,000 patients seen in BE, CA, CH, DE, EL, ES, FR, IE, LU, NL, NO, RO, SE, SI, TR, UK. The 2006 social & medical form have 92 unique questions. Until 2011 data was collected on paper then entered in a database, now a free internet-based mobile survey tool is used by all teams except 4. Data are centralized in France & analysis is externalized. Results Main challenges revolve around 3 axes: strategic decision making; technology and training; working within limitations. We developed solutions including standardized training sessions & tools, peer-evaluations, an adapted survey tool, an open-source data analysis & visualization platform. Key lessons learned include: the value of collecting health & social data from populations excluded from all surveys; how collecting data helps programs monitoring; the importance of reaching consensus with field teams in the construction & implementation of data collection. Discussion While training is a major element to successful survey conduct, future innovations in health informatics will continue to increase the feasibility and quality of data collection within limited resource humanitarian contexts. Key messages: Field teams’ expertise improve data collection acceptability & process It is worthwhile & feasible to collect data with vulnerable people even in extreme low resource context
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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 | 0.218 | 0.356 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".