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Record W4409415993 · doi:10.1101/2025.04.13.25323641

Quantifying Care, Qualifying Experiences: A Systematic Review of Measurement-Based Care in Psychiatry from Patient and Provider Perspectives

2025· review· en· W4409415993 on OpenAlexaff
Ayan Dey, Ze’ev Lewis, Joshua Posel, Ruihong Pan, Karen Wang

Bibliographic record

VenuemedRxiv · 2025
Typereview
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsPatient carePsychologySystematic reviewNursingMedicineMEDLINEPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Objective: This systematic review synthesizes clinician and patient perspectives on the benefits and drawbacks of measurement-based care (MBC) in psychiatry. Study Selection and Analysis: We searched Ovid MEDLINE, EMBASE, EBM Reviews, APA PsychINFO, and CINAHL databases from inception to January 2024. After screening 1643 titles and abstracts, 47 full papers were reviewed, and 23 studies were ultimately included. Quality assessment was conducted using the Mixed Methods Appraisal Tool, and key patterns were extracted using thematic analysis. Findings: The review reflects opinions of 746 patients and 2804 clinicians across various settings. Patients valued MBC for enhancing communication, self-awareness, and reducing stigma. However, they expressed concerns about the adequacy of measures in reflecting their clinical state and uncertainty about how responses influence treatment decisions. Clinicians appreciated MBC for improving patient involvement, tracking treatment response, and enhancing communication efficiency. Concerns included inadequate capture of clinical complexity, potential reporting biases, time constraints, insufficient training, and concerns with respect to data usage and privacy. Conclusions: While patients and clinicians recognize significant benefits, including enhanced communication, improved insight, and more structured clinical decision-making, they also identify important limitations. These include concerns about the adequacy of scales to capture complex clinical presentations, potential impacts on the therapeutic alliance, and increased administrative burden. Moving forward, successful integration of MBC into routine care will require addressing these challenges through improved clinician training, clear guidelines for interpretation, greater transparency with respect to how data will be used, and more seamless integration with existing clinical workflows.

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)0.045
metaresearch head score (Gemma)0.208
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.045
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.208
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0150.017
Science and technology studies0.0010.002
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.103
GPT teacher head0.353
Teacher spread0.250 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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