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Record W4311268371 · doi:10.24124/2022/59335

The lived experiences of women currently in addiction recovery in Mississauga, Ontario

2022· dissertation· en· W4311268371 on OpenAlexaffabout
Ariana Keykhosravani

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsThematic analysisAddictionLived experienceSet (abstract data type)Addiction treatmentPsychologyPandemicMedical educationMedicineNursingQualitative researchCoronavirus disease 2019 (COVID-19)PsychotherapistSociologyPsychiatryDiseaseSocial scienceComputer science

Abstract

fetched live from OpenAlex

This research explores women's experiences in recovery from addiction in a suburban area of Mississauga, Ontario (ON). In the suburbs drugs are not as localized as they might be in a city. The goal set out in this research was to better understand recovery from the women’s perspectives as well as identify barriers, challenges, and benefits they faced in recovery and treatment programs. The research offers suggestions on how this information could be considered when creating or adapting current recovery and treatment programs. The research may also help us understand and improve women’s entry, retention, and completion of treatment programs. Six participants participated in semi-structured interviews, four in person and two over the video platform Zoom due to the COVID-19 pandemic. Thematic analysis was conducted and four themes emerged: opinions on treatment programs, cravings and withdrawals, support, and recommendations to improve treatment programs.

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)0.003
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 score0.060
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0230.009
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
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 head0.133
GPT teacher head0.423
Teacher spread0.290 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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