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Record W4402166227 · doi:10.1521/jsyt.2024.43.1.26

“In Hopes of Becoming a Phoenix”: Application of Narrative Practices in Exploration of Preferred Identities of Youth Contemplating Suicide in North East India

2024· article· en· W4402166227 on OpenAlexvenueno aff
Tama Dey, Diptarup Chowdhury

Bibliographic record

VenueJournal of Systemic Therapies · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsPhoenixNarrativePsychologySociologyGender studiesGeographyArchaeologyArtLiterature

Abstract

fetched live from OpenAlex

Narrative practices can provide newer ways to engage with individuals and communities in suicide prevention. This study is a pilot project that examined the applicability of narrative ideas in developing in-depth interview (IDI) and focus group discussion (FGD) probes to explore the preferred identities of young people who have contemplated suicide in Northeast India. Narrative ideas were embedded during the development of IDI and FGD probes and tested on five participants. The data collection utilized an IDI informed by the “absent but implicit” map and an FGD that followed the “definitional ceremony” approach. The narrative therapeutic approach brought out the broader sociocultural contexts of suicide, identifying skills and knowledge that young people use to respond to issues and uncovering intentions, hopes, and values of their lives. The approach also unfolded their collective experiences. The study provides ways to support young people with experiences of suicidality in moving from problem-saturated identities to preferred identities.

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)0.004
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 score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.372
Teacher spread0.277 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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