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Record W1200814686 · doi:10.1177/070674371506000602

Improving Access to Psychosocial Treatments—Integrating Patient, Provider, and Systems Approaches

2015· letter· en· W1200814686 on OpenAlexaffvenueabout
Sagar V. Parikh

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2015
Typeletter
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychotherapistPsychosocialEnthusiasmMental healthPsychological interventionIngenuityPopulationPsychologyHealth carePsychiatryMedicineSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Psychotherapy works well, but our mental health care system does not. Those broad observations are underscored by 2 articles in this issue.1,2 One can certainly cite the broad evidence base in psychotherapy for mental disorders, but these 2 articles provide another type of endorsement-both the public in Quebec and psychiatrists in British Columbia vote with their feet, so to speak-for psychotherapy. Dezetter et al1 carefully surveyed nearly 1300 attendees at primary care clinics across Quebec on 3 occasions during a year, and found major unmet mental health needs, including major gaps in psychotherapy delivery. Hadjipavlou et al2 surveyed attitudes toward psychotherapy and patterns of psychotherapy delivery among psychiatrists in British Columbia and discovered high confidence in psychotherapy efficacy and high rates of delivery of individual psychotherapy, but with few people served owing to time constraints. Nevertheless, despite the evidence and the enthusiasm, the stark reality is that most patients cannot get, and most psychiatrists cannot give, enough psychotherapy. While the obstacle is largely ascribed to insufficient funding for psychotherapy provision, pleas for more resources are unlikely to succeed.Instead, we must improve the access to effective psychotherapeutic treatments by applying creativity and ingenuity. By using a population health approach, we can explore solutions that involve identifying interventions at multiple levels (patient, provider, and systems) and through multiple techniques and formats.3,4 One key tenet of this approach is stepped care, where simpler and cheaper interventions are used initially, while more elaborate and expensive treatments are saved for those in greatest need.5 Stepped care already has wide acceptance across medicine, and frequently has been suggested in psychiatric contexts, including substance abuse, mood disorders, and anxiety.6,7 Our (see Parikh et al8) own experience with recommendations for stepped care for bipolar disorder has led to research studies to compare different treatment methods with widely different costs and intensities, resulting in evidence that brief group psychoeducational interventions may be clinically equal, easier to use to treat larger numbers of clients, and far less expensive than the traditional 20-session dose of individual CBT.8Scaling psychosocial interventions broadly across disorders and society requires widening the focus of interventions and delineating the appropriate provider and venue for such treatments. To begin with, the broadest theoretical principles of effective psychosocial intervention, evidence particularly favours several approaches: MI, psychoeducation, CBT, IPT, and mindfulness-based interventions.9 From the service delivery research literature, key lessons include the value of self-directed strategies, including traditional self-help books, websites that offer interventions, care facilitators, and peer-support group interventions.10 The burgeoning patient-centred care movement echoes larger trends in society, facilitated by the Internet, that place emphasis on what patients want, not simply what service providers wish to offer.11 Affordability parallels scalability in all these musings; we must find solutions that allow us to reach many people within existing financial envelopes. Thus what kind of interventions might all these considerations evoke?Resuming a patient, provider, and systems approach, patient-level interventions might begin with understanding how to use brief MI principles as a first step for virtually all other interventions. Overwhelming evidence across disorders documents difficulty in starting and adhering to treatment recommendations. A robust literature also shows that even 2 sessions of MI leads to improved engagement in treatment, both in medical and in psychiatric disorders.12 Next, with the motivated patient, let us respect both patient empowerment principles and concede that most people seek (correctly! …

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)0.029
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.029
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0050.009
Scholarly communication0.0160.019
Open science0.0050.024
Research integrity0.0100.017
Insufficient payload (model declined to judge)0.0120.002

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 head0.071
GPT teacher head0.303
Teacher spread0.232 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2015
Admission routes3
Has abstractyes

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