The nature of informational continuity of care in general practice
Bibliographic record
Abstract
BACKGROUND: The availability of patient information to practitioners forms the basis of informational continuity of care. Changes in family practice that now encourage multiphysician clinics have meant that informational continuity of care has become crucial because it is likely that a patient will not continuously see the same doctor. Therefore a review of the nature of informational continuity is useful. AIM: To answer the question 'How is informational continuity developed in general practice?'. DESIGN OF STUDY: A rigorous systematic review of relevant electronic databases. METHOD: Databases were searched for articles answering the research question. Articles focused on family medicine and informational continuity of care were included. Data from reviewed articles were independently extracted and reviewed by two researchers. Conceptual and evidence-based articles were included. RESULTS: Initially, 193 articles were obtained from all five bibliographic databases; 57 were retained following title and abstract review. Of these, 34 articles were included in the final systematic review. Results show that informational continuity of care is developed using paper/electronic records and remembered information collectively, through a series of doctor-patient consultations over time. Obstacles to its development are practitioners not recording patient information and patients not disclosing important details. CONCLUSION: These findings have implications for newer styles of primary care that may have a negative impact in the successful management of chronic illnesses in particular.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".