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Record W4403175876 · doi:10.24124/2015/59558

Applying Indigenous ways of knowing and healing to clinical mental health work

2015· dissertation· en· W4403175876 on OpenAlexaboutno aff
Suki Athwal

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousWork (physics)Mental healthPsychologyMedicinePsychotherapistEngineeringMechanical engineeringBiology

Abstract

fetched live from OpenAlex

This practicum report details my experiences as a mental health clinician working at an Aboriginal Child and Youth Mental Health Services office located on Lekwungen Territory on South Vancouver Island, British Columbia. It includes an articulation of my theoretical framework and approach to social work practice, a literature review, and some discussion of my practicum learning goals, activities, and tasks. My main practicum goal centers on learning about Indigenous ways of knowing and healing as they relate to improving (mental) health and wellness. This practicum and report do not include research data, and instead focus on sharing the knowledge and skills that I acquired while further developing my clinical social work practice framework.

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)0.014
metaresearch head score (Gemma)0.010
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.022
Scholarly communication0.0110.005
Open science0.0020.014
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.161
GPT teacher head0.470
Teacher spread0.309 · 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
Published2015
Admission routes1
Has abstractyes

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