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Record W7037982996

First Nations Health Governance in British Columbia: Strategic Planning and Performance Reporting in the First Nations Health Authority

2023· dissertation· en· W7037982996 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2023
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicDiptera species taxonomy and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityStrategic planningContext (archaeology)Corporate governancePublic healthProfitability indexHealth care
DOInot available

Abstract

fetched live from OpenAlex

The low health status of First Nations people in the Province of British Columbia and persistent gaps in health outcomes as compared with other provincial residents led to the founding of the First Nations Health Authority (FNHA) in 2013. With less than a decade of operations, there have been limited evaluative studies assessing performance of this unique first-of-its-kind health authority in Canada that is run by and for First Nations. An in-depth document analysis of all FNHA service plans and annual reports to date shows the development of strategic planning and performance reporting processes in the context of a new and growing organization. A detailed analysis into this type of organizational literature is helpful in highlighting areas of misalignment and improving planning and reporting processes. In a public sector context, analysis of service plans and annual reports is particularly useful in assessing performance and accountability where the metric of profitability is not a consideration as it is with private-sector organizations.

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)0.012
metaresearch head score (Gemma)0.043
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.810

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0080.003
Scholarly communication0.0080.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.058
GPT teacher head0.293
Teacher spread0.235 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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