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A narrative inquiry: moving on from homelessness for individuals with a major mental illness

2009· article· en· W2087662497 on OpenAlexafffund
Helen Kirkpatrick, Carolyn Byrne

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcMaster UniversityOntario Tech UniversitySt. Joseph’s Healthcare Hamilton
FundersMcMaster University
KeywordsNarrativeMental illnessCouragePsychologyHousing FirstMental healthNarrative inquiryPsychiatryPolitical science

Abstract

fetched live from OpenAlex

This narrative study explores the experience of 'moving on' from homelessness for individuals with major mental illnesses, after they have obtained permanent housing with supports. Twelve participants were interviewed up to three times over 6 months. There were various routes to homelessness, participants were homeless for varying lengths of time, and they described different journeys of 'moving on' in their lives. Place, and a series of places, were central for participants in this experience. The experience of homelessness for many could be described as 'on the move', in a circular pattern from shelter to shelter or street. Permanent housing and supports allowed participants to 'move on', reconnecting with family, getting jobs and planning for the future. Several participants wanted their stories used to send messages of hope, courage and survival. This study highlights the need for nurses to be aware of the concept of 'place' in the process of recovery from mental illness.

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)0.008
metaresearch head score (Gemma)0.015
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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0120.011
Scholarly communication0.0060.009
Open science0.0020.009
Research integrity0.0020.005
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 head0.031
GPT teacher head0.419
Teacher spread0.388 · 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

Citations92
Published2009
Admission routes2
Has abstractyes

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