Conceptualizing patient-centered care for substance use disorder treatment: findings from a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: Despite ongoing efforts aimed to improve treatment engagement for people with substance-related disorders, evidence shows modest rates of utilization as well as client-perceived barriers to care. Patient-centered care (PCC) is one widely recognized approach that has been recommended as an evidence-based practice to improve the quality of substance use disorder treatment. PCC includes four core principles: a holistic and individualized focus to care, shared decision-making and enhanced therapeutic alliance. AIMS: This scoping review aimed to explore which PCC principles have been described and how they have defined and measured among people with substance-related disorders. METHODS: Following the iterative stages of the Arksey and O'Malley scoping review methodology, empirical (from Medline, Embase, PsycINFO, CINAHL and ISI Web of Science) and grey literature references were eligible if they focused on people accessing treatment for substance-related disorders and described PCC. Two reviewers independently screened the title/abstract and full-texts of references. Descriptive analyses and a directed content analysis were performed on extracted data. FINDINGS: One-hundred and forty-nine references met inclusion from the 2951 de-duplicated references screened. Therapeutic alliance was the most frequent principle of PCC described by references (72%); this was consistently defined by characteristics of empathy and non-judgment. Shared decision-making was identified in 36% of references and was primarily defined by client and provider strategies of negotiation in the treatment planning process. Individualized care was described by 30% of references and included individualized assessment and treatment delivery efforts. Holistic care was identified in 23% of references; it included an integrated delivery of substance use, health and psychosocial services via comprehensive care settings or coordination. Substance use and treatment engagement outcomes were most frequently described, regardless of PCC principle. CONCLUSIONS: This review represents a necessary first step to explore how PCC has been defined and measured for people accessing substance use disorder treatment. The directed content analysis revealed population and context-specific evidence regarding the defining characteristics of PCC-principles that can be used to further support the implementation of PCC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".