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Record W2131297453 · doi:10.12927/hcpol.2011.22121

How Has Health Services Research Made a Difference?

2011· article· en· W2131297453 on OpenAlexaffvenueabout
Steven Lewis

Bibliographic record

VenueHealthcare policy · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHealth careSociologyKnowledge translationResearch ethicsPolitical scienceEngineering ethicsLibrary scienceEngineeringKnowledge managementLaw

Abstract

fetched live from OpenAlex

Health services research (HSR) is commonly conceived as an applied discipline whose success is defined by its tangible impact on policy, practice or both. In Canada there has been a concerted effort to engage decision-makers in informing the research agenda. While it is admirable to aspire to practical utility, the HSR community has no control over the ultimate disposition of its work. Furthermore, the conditions for change must be present if the pathway from relevant, high-quality research to application is to be relatively smooth and immediate. In such cases, the changes may have occurred regardless of whether the research to support them took place. An examination of some widely renowned HSR reveals that timely and significant impact is relatively rare. Moreover, research that fundamentally changes how we view the world plays out over decades; it would be impossible to act on it in the short term, and in some cases it is not clear what ought to be done. The implications are that the first duty of HSR is to seek truth, and that funding and decision-making communities should define "useful" broadly, from a longer-term perspective. Taking the wide and the long view will in the end generate a greater return on investment in HSR than focusing too narrowly on contemporary preoccupations.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.404
GPT teacher head0.541
Teacher spread0.137 · 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

Citations5
Published2011
Admission routes3
Has abstractyes

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