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Record W2056025463 · doi:10.12927/hcpol.2011.22117

The Manitoba Centre for Health Policy: A Case Study

2011· article· fr· W2056025463 on OpenAlexvenueaboutno aff
Gail Marchessault

Bibliographic record

VenueHealthcare policy · 2011
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsExcellenceGovernment (linguistics)Qualitative researchScholarshipCredibilityPublic administrationTeamworkSustainabilityPolitical scienceStepping stonePublic relationsSociologyEconomic growthLawSocial science

Abstract

fetched live from OpenAlex

CONTEXT: The Manitoba Centre for Health Policy (MCHP) is a university research centre with a long-standing contractual arrangement with government. OBJECTIVE: The purpose of this project was to examine the facilitators and challenges in the development, establishment and continuation of MCHP. METHODS: In-depth, semi-structured interviews with 28 participants selected purposefully and a document review were conducted and analyzed using qualitative methods. RESULTS: Although a unique confluence of factors facilitated MCHP's establishment, participants viewed safeguards to credibility (arm's-length from government; guaranteed academic freedom) along with powerful advocates as key to longevity. Other factors that participants discussed as important to sustainability included excellence in scholarship; thorough protection of privacy; stable funding; incremental growth; teamwork; leadership; nurturing of relationships; and authentic partnerships. CONCLUSIONS: MCHP has demonstrated that using local administrative data to address policy-related research questions is of enduring value to local and provincial communities, and also has national and international relevance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.601

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0220.005
Scholarly communication0.0040.001
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.116
GPT teacher head0.397
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2011
Admission routes2
Has abstractyes

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