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Record W2041999673 · doi:10.1186/s12913-014-0545-x

How does context influence collaborative decision-making for health services planning, delivery and evaluation?

2014· article· en· W2041999673 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Health Services Research · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSt. Michael's HospitalMcMaster UniversityUniversity of TorontoUniversity Health Network
FundersCancer Care Ontario
KeywordsHealth informaticsMedicineNursing researchContext (archaeology)Health services researchHealth administrationKnowledge translationQualitative researchRelevance (law)IncentivePublic relationsMedical educationKnowledge managementNursingPublic healthPolitical scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Collaboration among researchers (clinician, non-clinician) and decision makers (managers, policy-makers, clinicians), referred to as integrated knowledge translation (IKT), enhances the relevance and use of research, leading to improved decision-making, policies, practice, and health care outcomes. However IKT is not widely practiced due to numerous challenges. This research explored how context influenced IKT as a means of identifying how IKT could be strengthened. METHODS: This research investigated IKT in three health services programs for colon cancer screening, prostate cancer diagnosis, and the treatment of pancreatic cancer. Qualitative methods were used to explore contextual factors that influenced how IKT occurred, and its impact. Data were collected between September 1, 2012 and May 15, 2013 from relevant documents, observation of meetings, and interviews with researchers and decision-makers, analyzed using qualitative methods, and integrated. RESULTS: Data were analyzed from 39 documents, observation of 6 meetings, and 36 interviews. IKT included interaction at meetings, joint undertaking of research, and development of guidelines. IKT was most prevalent in one program with leadership, clear goals, dedicated funding and other infrastructural resources, and an embedded researcher responsible for, and actively engaged in IKT. This program achieved a variety of social, research and health service outcomes despite mixed individual views about the value of IKT and the absence of a programmatic culture of IKT. Participants noted numerous challenges including lack of time and incentives, and recommendations to support IKT. A conceptual framework of factors that influence IKT and associated outcomes was generated, and can be used by others to plan or evaluate IKT. CONCLUSIONS: The findings can be applied by researchers, clinicians, managers or policy-makers to plan or improve collaborative decision-making for health services planning, delivery, evaluation or quality improvement. Further research is needed to explore whether these findings are widespread, and further understand how IKT can be optimized.

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.

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)0.038
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0380.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.360
GPT teacher head0.692
Teacher spread0.332 · 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