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Record W7134236197 · doi:10.26181/16641655

Activating Partnership Assets to Produce Synergy in Primary Health Care: A Mixed Methods Study

2021· article· W7134236197 on OpenAlexaboutno aff
Katya Loban, Catherine M. Scott, Virginia Lewis, Susan Law, Jeannie Haggerty

Bibliographic record

VenueLa Trobe University · 2021
Typearticle
Language
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipPrimary careAdaptation (eye)Limited partnershipAction (physics)Qualitative researchHealth care

Abstract

fetched live from OpenAlex

Partnerships are an important mechanism to tackle complex problems that extend beyond traditional organizational divides. Partnerships are widely endorsed, but there is a need to strengthen the evidence base relating to claims of their effectiveness. This article presents findings from a mixed methods study conducted with the aim of understanding partnership processes and how various partnership factors contribute to partnership effectiveness. The study involved five multi-stakeholder partnerships in Canada and Australia working towards improving accessibility to primary health care for vulnerable populations. Qualitative data were collected through the observa-tion of 14 partnership meetings and individual semi-structured interviews (n = 16) and informed the adaptation of an existing Partnership Self-Assessment Tool. The instrument was administered to five partnerships (n = 54). The results highlight partnership complexity and the dynamic and contingent nature of partnership processes. Synergistic action among multiple stakeholders was achieved through enabling processes at the interpersonal, operational and system levels. Synergy was associated with partnership leadership, administration and management, decision-making, the ability of partnerships to optimize the involvement of partners and the sufficiency of non-financial resources. The Partnership Synergy framework was useful in assessing the intermediate outcomes of ongoing partnerships when it was too early to assess the achievement of long-term intended outcomes.

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)0.007
metaresearch head score (Gemma)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.003
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 head0.080
GPT teacher head0.441
Teacher spread0.361 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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