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Record W2567164440 · doi:10.1017/s1463423616000438

Variation matters and should be included in health care research for comparison of outcomes

2016· article· en· W2567164440 on OpenAlexaff
Chris van Weel, Robyn Tamblyn, Deborah Turnbull

Bibliographic record

VenuePrimary Health Care Research & Development · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcGill UniversityInstitute of Health Services and Policy ResearchCanadian Institutes of Health Research
Fundersnot available
KeywordsHealth carePsychological interventionContext (archaeology)Set (abstract data type)PsychologyMedicineNursingPublic economicsEconomic growthGeographyEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Health care is provided under the conditions in which people live and under the rules and regulations of a prevailing health system. As a consequence, 'local' circumstances are an important determinant of the actual care that can be provided and its effects on the health of individuals and populations. This plays in particular, but not exclusively, a role in community-based primary health care. Although this is generally accepted, there is little insight in the impact of the setting and context in which health care is provided on the outcome of care. Aim This paper argues the case to use this natural variation within and between countries as an opportunity to be used as a form of natural experiment in health research. Arguments We argue that analysing and comparing outcomes across settings, that is comparative outcomes of interventions that have been performed under different health care conditions will improve the understanding of how the real-life setting in which health care is provided - including the health system, the socio-economic circumstances and prevailing cultural values - do determine outcome of care. Recommendations To facilitate comparison of research findings across health systems and different socio-economic and cultural contexts, we recommend a more detailed reporting of the conditions and circumstances under which health research has been performed. A set of core variables is proposed for studies in primary health care.

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)0.020
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.307
GPT teacher head0.593
Teacher spread0.286 · 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.

Study designObservational
Domainnot available
GenreCommentary

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

Citations6
Published2016
Admission routes1
Has abstractyes

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