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Record W2117170298 · doi:10.1177/001789690206100403

The meaning of'health improvement'

2002· article· en· W2117170298 on OpenAlexaff
Stephen Abbott, Dominique Florin, Naomi Fulop, Stephen Gillam

Bibliographic record

VenueHealth Education Journal · 2002
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsMeaning (existential)Government (linguistics)Health careInequalityQualitative researchPopulation healthPublic relationsPopulationNursingHealth policyMedicinePsychologyPublic healthSociologyPolitical scienceEnvironmental healthSocial science

Abstract

fetched live from OpenAlex

Objective To explore how those working in and with primary care organisations understand the term 'health improvement'. Design Six qualitative case studies. Setting Primary Care Groups and Trusts and partner organisations. Method Semi-structured interviews with senior personnel from participating organisations. Results Informants offered a wide range of definitions which generally combined more than one meaning. Replies ranged from, on the one hand, an emphasis on National Health Service (NHS) service provision to, on the other, an emphasis on the socioeconomic determinants of population health, or the quality of life of individuals. Some explained the term primarily as a government strategy; some, as a set of activities for the NHS; some, in terms of the overarching purpose of health improvement. Conclusion Our informants offered more detailed definitions of 'health improvement' than are articulated in Department of Health documents. However, they did not include health inequalities in their definition. Recent government announcements about health inequalities targets may help to ensure that all primary care organisations consider the inequalities in the health of their population.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.450
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.116
GPT teacher head0.480
Teacher spread0.364 · 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 designNot applicable
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

Citations1
Published2002
Admission routes1
Has abstractyes

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