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Record W1926629664

Continuity of care for older patients in family practice: how important is it?

2006· article· en· W1926629664 on OpenAlexaffabout
Graham Worrall, John Knight

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCINAHLObservational studyMEDLINEMedicineInterpersonal communicationHealth careFamily medicineRandomized controlled trialNursingGerontologyPsychologyPsychological interventionPathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the evidence that continuity of primary care is important for older people with chronic diseases. DATA SOURCES: MEDLINE, EMBASE and CINAHL were searched from January 1970 to June 2005 for original articles in English that examined the relationship between interpersonal continuity of patient care and health outcomes of people 50 years old and older. Articles found were reviewed and analyzed by both authors to assess the strength of study design and the quality of the evidence provided. STUDY SELECTION: We used the search terms "continuity of patient care," "elderly," "primary care," and "outcomes." Criteria from the Canadian Task Force on Preventive Health Care were used to assess the quality of studies; only studies providing levels I to III evidence were included in this review. SYNTHESIS: Of 7563 articles found, we chose 99 studies (and 27 other studies cited in them) by studying their abstracts. Assessment of these 126 studies indicated that only 5 were of good quality and relevant to the inquiry. Two of these 5 were randomized controlled trials, and 3 were observational studies. CONCLUSION: Although the literature on continuity of care generally suggests that continuity of interpersonal primary care is important and beneficial, specific evidence that it is beneficial for elderly people is scanty. There is a need for well designed studies to investigate this issue.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.382
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2006
Admission routes2
Has abstractyes

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