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Record W2313905730 · doi:10.7870/cjcmh-2000-0007

Being Indian: Strengths Sustaining First Nations Peoples in Saskatchewan Residential Schools

2000· article· en· W2313905730 on OpenAlexaffvenueabout
Isabelle D. Hanson, Mary Rucklos Hampton

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of ReginaSaskatchewan Polytechnic
Fundersnot available
KeywordsPrideAutonomyStrengths and weaknessesMental healthCompassionNarrativeSense of communitySpiritualityQualitative researchPsychologySociologyPolitical scienceMedicineSocial scienceSocial psychologyAlternative medicinePsychiatryLaw

Abstract

fetched live from OpenAlex

This qualitative study asked the question: what were the strengths that contributed to the survival of First Nations peoples during their stay in residential schools? Six elders who are survivors of residential schools in southern Saskatchewan were asked to respond in narrative form to this research question. Analysis of interviews revealed that, drawing on community-building skills of First Nations cultures, they created their own community with each other within the confines of this oppressive environment. The strengths they identified are consistent with sense of community identified in community psychological literature, yet are also unique to First Nations cultures. These strengths are: autonomy of will and spirit, sharing, respect, acceptance, a strong sense of spirituality, humour, compassion, and cultural pride. It is suggested that community-based mental health initiatives which identify traditional sources of strengths within First Nations communities will be most effective in promoting healing from residential school trauma.

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)0.003
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 score0.767
Threshold uncertainty score0.463

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0150.009
Scholarly communication0.0040.002
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.314
Teacher spread0.301 · 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

Citations26
Published2000
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Community Mental HealthSame topicIndigenous Health, Education, and RightsFrench-language works237,207