Multi-source synthesis of data to inform health policy
Bibliographic record
Abstract
OBJECTIVES: To propose a new method for comparing and integrating original qualitative data with systematic reviews of quantitative and qualitative studies, demonstrated by a study of the psychosocial needs of chronic fatigue syndrome (CFS) sufferers in Québec. METHODS: A systematic literature review was performed across various databases for English and French language studies, on the psychosocial aspects of CFS. Qualitative, quantitative, and mixed method studies published between January 1994 and July 2008 were included. Unpublished literature and reference lists of included studies were also searched. Themes identified in the literature were used to guide semi-structured interviews with seventeen CFS-sufferers, mostly recruited from a large specialist practice in Montreal. Interviews were transcribed verbatim and validated by a research assistant. Transcripts were coded using the identified themes. New codes were created when new issues arose. All themes were subsequently synthesized into overall categories using a constant comparative method. RESULTS: The literature search yielded thirty-one papers: twenty-eight primary studies and three systematic reviews. Twelve themes were identified and synthesized into four overall problem categories, such as "Lack of professional recognition." Interviews confirmed findings from the literature, but also revealed unidentified needs specific to CFS-sufferers in Québec. Policy recommendations were provided to address these needs. CONCLUSIONS: Multi-Source Synthesis provides a systematic method for synthesizing data from original studies with literature findings, thereby broadening the knowledge base and the local relevance of decisions concerning specific patient populations.
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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 | 0.447 | 0.600 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.075 | 0.062 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".