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Record W2128645079 · doi:10.3109/13561820.2011.577626

Producing and translating health system evidence for improved global health

2012· letter· en· W2128645079 on OpenAlexaff
Steven J. Hoffman, Julio Frenk

Bibliographic record

VenueJournal of Interprofessional Care · 2012
Typeletter
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHealthcare systemPsychologyHealth careMedicinePolitical science

Abstract

fetched live from OpenAlex

Dean of the Faculty, Harvard School of Public Health and T & G Angelopoulos Professor ofPublic Health and International Development, Harvard Kennedy School and Harvard School of Public Health, Boston, MA, USAEvidence is mounting to suggest that interprofessionalcollaboration is an innovative strategy that governmentsworldwide can use to strengthen their national healthsystems and improve population health outcomes (WHO,2010). From a global health perspective, interprofessionalcollaboration offers a way to synergistically maximize thecontributions of every available health worker whileconserving limited resources. Interprofessional collaborationis also said to result in more flexible health workforces thatare better prepared to tackle unexpected challenges. Evidencefrom empirical studies and systematic reviews highlightseveral system-wide benefits of interprofessional collabor-ation which are relevant to achieving global health goals.These include improved quality of services, access to care andpatient safety as well as reductions in costs, hospitaladmissions and mortalities (CHSRF, 2006; Reeves, Goldman,Burton, & Sawatzky-Girling, 2010; Reeves, et al., 2008;Zwarenstein, Goldman, & Reeves, 2009). Interprofessionalcollaboration is also increasingly recognized as an importantpart of broader efforts to strengthen national health systems.Calls for transforming the way health professionals areeducated and the manner in which they practice areaccordingly growing louder (Frenk et al., 2010).However, equally important to education and practicereforms are the health system policies that must be enacted toenable, support and sustain interprofessional collaboration.Various mechanisms have recently been demonstrated toinfluence the success or failure of interprofessionalcollaboration in national health systems. The World HealthOrganization, for example, recently emphasized howsupportive funding streams, remuneration models, capitalplanning, regulation, professional registration, accreditationand risk management can contribute to effective inter-professional collaboration (WHO, 2010). The importanceof appropriate clinical governance models, nationalhealth legislation, integrated information systems andcommunication platforms has also recently been highlighted(Mickan, Hoffman, & Nasmith, 2010).At this point in time, very little research or otherknowledge is available on whether, how and why particularhealth system reforms aiming to promote interprofess-ional collaboration actually achieve positive outcomes.Considerable research has been amassed that focuses onindividual- and institution-level interventions and out-comes, but few inquiries have been conducted at the systemlevel of analysis. In particular, policymakers often ask forevidence on the comparative effectiveness, cost and likelystakeholder responses to implementing the various healthsystem interventions presented to them. Increasing theproduction of high-quality, policy-relevant and locallyapplicable health system evidence on interprofessionalcollaboration is therefore a strategic opportunity for theinterprofessional community to make a significant contri-bution to national and global health efforts. More of thisknowledge will help shape whether and how governmentsworldwide invest in this strategy.In terms of actually producing this knowledge, it is truethat health systems are complex and challenging to study, butthey are certainly not “black boxes” that are too complicatedor intricate to understand. Everyday new knowledge isuncovered on what works and what does not work, and why,in different health system contexts (Frenk, 2010). What isunfortunate is that too often opportunities to evaluate healthsystem reforms are not seized. This appears to be the casefor interprofessional collaboration: the past decade haswitnessed various major system-wide initiatives on nearlyevery continent, yet very few of them have been rigorouslyevaluated. Indeed, while efforts to support interprofessionalcollaboration have been reported in at least 41 countriesworldwide (Rodger & Hoffman, 2010), knowledge of the

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
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 score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.004
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 head0.073
GPT teacher head0.502
Teacher spread0.429 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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