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Record W6964389268 · doi:10.25316/ir-14906

Using design thinking to assess needs and develop faith-based and leisure programming for women of lived experience with homelessness

2019· other· en· W6964389268 on OpenAlexaboutno aff

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2019
Typeother
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsLived experienceParticipatory action researchRecreationAction (physics)Citizen journalismQualitative researchPhoto elicitationDesign thinkingAction research

Abstract

fetched live from OpenAlex

Homelessness is a critical issue across Canada. Providing housing and other basic services to individuals of lived experience with homelessness is a priority for agencies interested in addressing this issue. However, once basic needs have been met, provision of leisure and faith-based programming could contribute to holistic improvements in health and well-being for this homeless population. The purpose of this research is to empathize with and identify the needs of women of lived experience with homelessness living in Nanaimo’s Samaritan House (inclusive of Martha’s Place). Guided by design thinking and participatory action approaches (i.e., user-centered, empathetic, co-created design), qualitative data was collected from residents of the Samaritan House and Martha’s Place during the summer of 2019. The findings of the research will be used by Island Crisis Care Society (the non-profit that owns and operates the Samaritan House) to improve resident wellbeing by co-addressing their leisure and faith-based needs through their own efforts and via community partnerships. Our findings indicate that supporting leisure and recreational participation as well as opportunities to engage in faith-based activities is both desired and seen as key to supporting overall health and wellbeing.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.228
Teacher spread0.193 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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