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Record W2784865451 · doi:10.1177/0008417417719725

Occupational needs and priorities of women experiencing homelessness

2017· article· en· W2784865451 on OpenAlexfundvenueaboutno aff
Sofia Salsi, Yara Awadallah, Aurélie B. Leclair, Marie-Lyse Breault, Dan-Tam Duong, Laurence Roy

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersFaculty of Medicine, McGill University
KeywordsOccupational therapyPsychologyMedicineGerontologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Women in unstable housing or who are homeless experience disruptions of occupational engagement and performance. Little is known about their perspective on their occupational needs and priorities. PURPOSE: This study aimed to determine how patterns of occupational engagement and performance are facilitated or hindered by personal factors and resources available to sheltered women. METHOD: A participatory, descriptive mixed-methods study design was used. Twenty-one residents were interviewed using the Canadian Occupational Performance Measure, the Goal Attainment Scale, and a time-use inventory. Nineteen residents participated in semistructured qualitative interviews. Qualitative data were analysed using interpretive description. FINDINGS: Participants spent most time on sleep and passive leisure and identified occupational performance issues and goals related to active leisure and employment. Participants' occupational lives were described as transitional journeys comprising five subthemes: seeking safety and stability, being sheltered, shaping one's identity, developing resilience, and engaging in contemplation, contribution, and connectedness through occupation. IMPLICATIONS: Trauma-informed and strengths-based approaches are recommended for this population. Future research should study the implementation of occupation-based interventions in community settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.281
GPT teacher head0.503
Teacher spread0.223 · 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 teacher head, not a consensus.

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

Citations19
Published2017
Admission routes3
Has abstractyes

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