MétaCan
Menu
← Back to cohort
Record W7132889374

Shelters- the life within: the impact of emergency shelters on the mental health of single homeless women

2004· dissertation· W7132889374 on OpenAlexaboutno aff
Rachel Singer

Bibliographic record

VenueTSpace · 2004
Typedissertation
Language
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthQualitative researchDistressInterpretative phenomenological analysisMental health servicePower (physics)Psychological distressMental distress
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines how single homeless women experienced emergency shelters within the City of Toronto. In view of significant gaps in existing literature, the goal of this research was to reveal how the environment within emergency shelters affected participants' mental health and well-being. This was a phenomenological study that drew on twenty-four in-depth qualitative interviews with single homeless women. The findings indicated that women experienced power dynamics, distress and poor mental health while staying in emergency shelters. The women interviewed had many recommendations for changing the emergency shelter system in Toronto and ending homelessness. This study provides significant contributions to the homelessness and health literature and is useful for policy analysts, shelter operators and the social service sector. It is hoped that the experiences of participants will be a catalyst for reflection and change to improve the life conditions of single homeless women.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.005
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.071
GPT teacher head0.454
Teacher spread0.383 · 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 designObservational
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
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueTSpace→Same topicHomelessness and Social Issues→French-language works237,207→