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Record W4322758860 · doi:10.1097/jan.0000000000000511

The Experience of Women With Opioid Use Disorder Accessing Methadone Treatment

2023· article· en· W4322758860 on OpenAlexaffabout
Lizette C. Keenan, Maria M. Ojeda, Anna Valdez

Bibliographic record

VenueJournal of Addictions Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsOpioid use disorderMethadonePhenomenology (philosophy)Opiate Substitution TreatmentInterpretative phenomenological analysisHealth careLived experiencePsychologyPsychiatryPhenomenonMedicineOpioidNursingQualitative researchPsychotherapistSociologyBuprenorphinePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT: The number of women experiencing opioid use disorder (OUD) in Canada has increased exponentially. In Canada, healthcare is socialized and free for all citizens and, often, medications like methadone are free as well, yet few individuals with OUD access treatment services. The purpose of this study was to describe the lived experiences of Canadian women with OUD who were receiving methadone treatment. Interpretive phenomenology was used to investigate the treatment experiences of seven women with OUD. The conceptual framework of self-care of chronic illness was used to examine this phenomenon. Data were analyzed using a seven-step process of interpretive phenomenological analysis. Four major themes emerged: learning how to be you again, reaching out for help, finding your way to methadone, and going down the path of methadone. Women's experiences were influenced by family, friends, and healthcare providers. Accessibility and self-determination were important factors in entering and sustaining treatment. This study contributes to the discipline of nursing by providing accurate information regarding women's experiences with OUD and uncovering practice changes that can attract and retain women in treatment.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.330
Teacher spread0.304 · 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 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

Citations3
Published2023
Admission routes2
Has abstractyes

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Same venueJournal of Addictions NursingSame topicOpioid Use Disorder TreatmentFrench-language works237,207