Characterising generalism in clinical practice: a systematic mixed studies review protocol
Bibliographic record
Abstract
BACKGROUND: Generalist physician care is associated with improved patient outcomes. Despite initiatives to promote generalism in educational settings, recruitment to generalist disciplines remains less than required to serve societal needs. Increasingly this impacts not just general practice but also generalist specialties such as internal medicine, surgery, and paediatrics. One potential factor for this deficit is a lack of explicit attention to generalism as a praxis, including clarifying key aspects of generalist expertise. AIM: To examine empirical clinical literature on generalism, and characterise how generalism is described and delivered by physicians in primary and secondary care. DESIGN & SETTING: A systematic mixed studies review (SMSR) including quantitative, qualitative, mixed-methods studies, and systematic reviews of physician generalist practice. METHOD: MEDLINE, Psycinfo, SocINDEX, Embase, Ovid HealthSTAR, Scopus, and Web of Science will be searched for English language studies from 1999 to present, using a structured search. Given study heterogeneity, quality appraisal will not be performed. Two reviewers will perform study selection for each study. Data extraction will focus on how generalism is defined and characterised, including the clinical care provided by generalists and patient experiences of generalist care. Quantitative and qualitative data will be summarised in tabular and narrative form. Convergent synthesis design will then be used to synthesise quantitative and qualitative data. CONCLUSION: Findings will characterise generalism and generalist practice from a grassroots clinical perspective. By identifying similarities and differences across generalist disciplines, this work will inform more focused educational initiatives on generalism at undergraduate and postgraduate level, including collaborations between generalist disciplines.
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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 | 0.150 | 0.122 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.021 | 0.017 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.078 | 0.015 |
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".