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Record W2893933559 · doi:10.1177/1049732318798353

“I Haven’t Given Up and I’m Not Gonna”: A Phenomenographic Exploration of Resilience Among Individuals Experiencing Homelessness

2018· article· en· W2893933559 on OpenAlexafffund
Sneha Shankar, Evie Gogosis, Anita Palepu, Anne Gadermann, Stephen W. Hwang

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of TorontoSt. Michael's HospitalCentre for Advancing Health OutcomesProvidence Health Care Research InstituteProvidence Health CareUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsPhenomenographyPhenomenonConstruct (python library)PsychologyPsychological resilienceSituatedQualitative researchPsychological interventionSocial psychologyDevelopmental psychologySafe havenSociologyEpistemologyPedagogySocial science

Abstract

fetched live from OpenAlex

Resilience is a factor related to positive health outcomes. Exploring this concept among adults experiencing homelessness can inform interventions while subsequently considering individuals’ strengths. A phenomenographic approach was applied to examine this concept among a sample of 22 individuals involved in qualitative interviews. The phenomenographic inquiry identified eight conceptions and found resilience is captured in both positive and negative ways. Conceptions are summarized by two categories, situated in an outcome space which describes the overall resilience experience and the different ways these conceptions are understood and experienced. Categories summarize conceptions as Staying Strong and Sustaining Positive Beliefs, which highlight the construct as being captured by a persistent positive aspect; however, the findings also uniquely describe the influence of negative conceptions toward the overall phenomenon. The findings suggest resilience is recognizable during adversity, and it is a phenomenon that has the potential to be strengthened.

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)0.018
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.499
GPT teacher head0.619
Teacher spread0.120 · 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 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

Citations14
Published2018
Admission routes2
Has abstractyes

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