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Record W4386248215 · doi:10.1016/j.josat.2023.209154

The mobilization of nurse-client therapeutic relationships in injectable opioid agonist treatment: Autonomy, advocacy and action

2023· article· en· W4386248215 on OpenAlexafffund
Scott Harrison, David Byres, Julie Foreman, Sherif Amara, Wistaria Burdge, Scott Macdonald, Martin T. Schechter, Eugenia Oviedo‐Joekes

Bibliographic record

VenueJournal of Substance Use and Addiction Treatment · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsFraser HealthCentre for Advancing Health OutcomesSt. Paul's HospitalUniversity of British ColumbiaProvincial Health Services AuthorityProvidence Health Care
FundersCanadian Institutes of Health ResearchCanada Research ChairsCanada Foundation for Innovation
KeywordsAutonomyEmpathyNursingGrounded theoryPsychologyHealth careCoding (social sciences)MedicineSocial psychologyQualitative researchSociologyPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: Injectable opioid agonist treatment (iOAT) is an evidence-based treatment that serves an important minority of people with opioid use disorder who require specialized care. Unique to iOAT care is the consistency with which clients access treatment (up to three times daily), a condition that creates repeated opportunities for health care engagement. To date, no study has examined therapeutic relationships in this life saving, nurse-led treatment that can have lasting implications in the equitable delivery of other forms of addictions care. METHODS: This study used grounded theory to generate a dynamic framework for therapeutic relationship building in iOAT. Researchers collected semi-structured interviews from registered nurses working in iOAT sites (n=24) form January 2020 through June 2022. The study analyzed collected data through a constant comparative analysis; explored through open, axial, and selective coding; and assessed in a conditional relationship matrix. The team reviewed key findings with stakeholders through formalized processes of engagement to confirm saturation of coding categories. Throughout data collection and analysis, researchers integrated feedback from additional knowledge users and member checking. Reported findings adhered to the COREQ1 standardized checklist. RESULTS: We identified five interrelated categories that created a distinct culture of care for iOAT nurses: Ways of Knowing, Personal Investment, Leveraging Empathy, Finding Flexibility, and Collaborating to Overcome. Through creating a safe, nonjudgmental environment, nurses establish therapeutic relationships that build trust to identify client needs outside of medication administration. In turn, nurses participate in team-based problem solving to advocate for client needs. If nurses cannot find flexibility within and outside of the health care system to improve client engagement, tensions can arise and therapeutic relationships can be strained. CONCLUSIONS: Therapeutic relationships are an integral part of building and maintaining trust with a population that has been precariously involved with other forms of health care. Nurses make a substantial effort to create a safe and nonjudgmental environment to manifest a culture of care that bridges client needs and program access. Without the expansion of access to iOAT programs and their embedded services, nurses are limited in their ability to provide individualized care for clients with diverse needs.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.438

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 head0.053
GPT teacher head0.301
Teacher spread0.247 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

Explore more

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