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Record W2993504730 · doi:10.12927/hcq.2013.23316

Partnership and Measurement: The Promise, Practice and Theory of a Successful Health Social Networking Strategy

2013· article· en· W2993504730 on OpenAlexaff
Terrence J. Montague, Joanna Nemis‐White, Bonnie S. Cochrane, Janice Meisner, Tessa Trasler

Bibliographic record

VenueHealthcare Quarterly · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsBioPhage Pharma (Canada)Nova Scotia Health AuthorityNewfoundland and Labrador Centre for Applied Health ResearchCARE Canada
Fundersnot available
KeywordsMultidisciplinary approachGeneral partnershipCasualHealth careBest practicePsychological interventionMedicineNursingPublic relationsBusinessPremisePolitical science

Abstract

fetched live from OpenAlex

Patient health management (PHM) was launched as a promising paradigm to close care gaps, the inequities between usual and best care, for whole patient populations. PHM's core premise was that interventions of multidisciplinary, community-oriented partnerships that used repeated measurement and feedback of provider practices, clinical and economic outcomes and general communication of relevant health knowledge to all stakeholders would continuously make things better. This article reviews the evolution of PHM from its genesis in a series of casual hospital-based networks to its maturation in a province-wide, community-focused, clustered-lattice social network that facilitated the improved clinical and cost-efficient care and outcomes of whole patient populations. The factors underlying PHM's clinical and cost efficacy, specifically its patient-centric social networking structures and integral measurement and knowledge translation processes, offer continuing promise to optimally manage the care of our increasingly aged patient populations, with their high burden of chronic diseases and disproportionately large care gaps. In an era when patients are demanding and leading change, and governments are struggling fiscally, PHM's clinical efficacy and cost-efficiency are especially resonant. Things can be better.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.057
GPT teacher head0.294
Teacher spread0.237 · 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 designOther design
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

Citations3
Published2013
Admission routes1
Has abstractyes

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