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Record W2744731709 · doi:10.1177/1355819617714815

Health services research: building capacity to meet the needs of the health care system

2017· article· en· W2744731709 on OpenAlexaff
Helen Barratt, Jay Shaw, Lisa Simpson, R. Sacha Bhatia, Naomi Fulop

Bibliographic record

VenueJournal of Health Services Research & Policy · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsWomen's College Hospital
FundersNational Institute for Health and Care Research
KeywordsGeneral partnershipWork (physics)Process (computing)Health careBusinessQuality (philosophy)Knowledge managementPublic relationsSet (abstract data type)Process managementComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Health services researchers have an important role to play in helping health care systems around the world provide high quality, affordable services. However, gaps between the best evidence and current practice suggest that researchers need to work in new ways. The production of research that meets the needs and priorities of the health system requires researchers to work in partnership with decision-makers to conduct research and then mobilize the findings. To do this effectively, researchers require a new set of skills that are not conventionally taught as part of doctoral research programmes. In addition to wider contextual changes, researchers need to understand better the needs of decision-makers, for example through short placements in health system decision-making settings. Second, researchers need to learn to accommodate those needs throughout the research process, including identifying research needs; conducting research collaboratively with decision-makers and producing effective research products.

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)0.165
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1650.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.002
Science and technology studies0.0070.000
Scholarly communication0.0010.001
Open science0.0050.001
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 head0.525
GPT teacher head0.575
Teacher spread0.050 · 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 designQualitative
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

Citations37
Published2017
Admission routes1
Has abstractyes

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