A realist review of understanding how community-level oral health promotion programs for humanitarian migrants work, in which contexts, and why: initial program theories
Bibliographic record
Abstract
Humanitarian migrants are people who are forced to move away from their habitual residence. Experiencing difficult migration trajectories leaves them in vulnerable conditions which can result in poor general health. They often experience oral health problems which can decrease their quality of life. Oral health is crucial for enabling humanitarian migrants to adapt to their new lives in their host countries. Thus, community organizations develop oral health promotion programs for humanitarian migrants in three categories of oral health education programs, dental service provision programs, and community oral health worker (COHW) programs. In order to optimize successful development and implementation of these programs, there is a need to evaluate how these programs work in practice and which factors helped or inhibited them to achieve their desired outcomes.McGill’s Migrant Oral Health Program (MOHP), led by Dr. Mary Ellen Macdonald and Dr. Belinda Nicolau, has designed a realist synthesis to understand how community-level oral health promotion programs for humanitarian migrants work, under which circumstances, and why (1). A realist review is an approach to develop causal explanation of observed outcomes in context-mechanism-outcome (CMO) configurations. CMOs explain how an outcome is caused by the mechanisms which are activated in a specific context. These CMOs form the basis for a program theory which will be evolved throughout the research.Following Pawson's five stages for conducting realist reviews, the MOHP team led by MSc student Negin Eslamiamirabadi, clarified the scope of review and started developing an initial program theory (IPT) (1), beginning with oral health education programs. My thesis project follows, advancing the IPTs for dental service provision programs and COHWs, andivintegrating all IPTs in order to set up the next step that is, sharing the IPTs with stakeholders and experts for feedback and finalizing.This thesis explains realist review methodology and describes dimensions of initial theorizing. It illustrates how to create a set of IPTs. By identifying and reviewing 17 data sources in Google Scholar relevant to the oral health promotion programs for humanitarian migrants, this thesis presents 29 IPTs. In addition to highlighting some noteworthy aspects of contextual factors and mechanisms, I also discuss theories and assumptions which are novel and beneficial to enhance oral health promotion programs for humanitarian migrants.This thesis contributes important work for MOHP’s realist review process. The ultimate goal of our completed review is to advance a program theory for community-level oral health promotion programs for humanitarian migrants
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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 | 0.038 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".