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Record W4388570640 · doi:10.33137/tijih.v1i3.38474

“No matter how many times you fall, they’ll still give you another opportunity” – Conversations with Key Informants to Evaluate a Community-led Rehabilitation Facility in Northern Saskatchewan

2023· article· en· W4388570640 on OpenAlexaffabout
Jessica Froehlich, Subhashini Iyer, Kehinde Ametepee, Kimberly Smith, Victor Foshion, Walter P. Smith, Anne Mease, Alexandra King, Malcolm King

Bibliographic record

VenueTurtle Island Journal of Indigenous Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsSaskatchewan Health AuthorityUniversity of Saskatchewan
Fundersnot available
KeywordsIndigenousContext (archaeology)Public relationsKey (lock)Process (computing)SociologyCommunity engagementPolitical scienceGeographyComputer scienceEcology

Abstract

fetched live from OpenAlex

Community engagement processes conducted with the Northern Village of Pinehouse determined the need for an evaluation of the Recovery Lake Program (RLP). The study exemplifies a collaborative process of sharing experiences and stories, inclusive of key informants from varied backgrounds and expertise, affording the opportunity to weave together Indigenous knowledge, research and policy expertise in a voluntary, non-hierarchical context. The stories shared throughout the interviews offer wisdom that will inform the realignment and direction of growth of some of the facilities and programs offered by the RLP. Through these stories, themes that highlight good practices, positive components and several gaps in the program emerge. Finally, the article includes recommendations for improving the RLP’s treatment and post-program support needs for their clients.

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)0.005
metaresearch head score (Gemma)0.005
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 score0.763
Threshold uncertainty score0.472

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.007
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0020.004
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 head0.020
GPT teacher head0.300
Teacher spread0.279 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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