A global picture of family medicine: the view from a WONCA Storybooth
Bibliographic record
Abstract
Abstract Background Family Medicine is a novel discipline in many countries, where the motivation for training and value added to communities is not well-described. Our purpose was to understand the reason behind the choice of Family Medicine as a profession, the impact of Family Medicine on communities, and Family Medicine's characterizing qualities, as perceived by family doctors around the world. Methods One-question video interviews were conducted using an appreciative inquiry approach, with volunteer participants at the 2016 World Organization of Family Doctors conference in Rio de Janeiro. Qualitative data analysis applied the thematic, framework method. Results 135 family doctors from 55 countries participated in this study. Three overarching themes emerged: 1) key attributes of Family Medicine, 2) core Family Medicine values and 3) shared traits of family doctors. Family Medicine attributes and values were the key expressed motivators to join Family Medicine as a profession and were also among expressed factors that contributed to the impact of Family Medicine globally. Major sub-themes included the principles of comprehensive care, holistic care, continuity of care, patient centeredness, and the patient-provider relationship. Participants emphasized the importance of universal care, human rights, social justice and health equity. Conclusion Family doctors around the world shared stories about their profession, presenting a heterogeneous picture of global Family Medicine unified by its attributes and values. These stories may inspire and serve as positive examples for Family Medicine programs, prospective students, advocates and other stakeholders.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".