Improving Access to Psychosocial Treatments—Integrating Patient, Provider, and Systems Approaches
Notice bibliographique
Résumé
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! …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,029 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,009 |
| Communication savante | 0,016 | 0,019 |
| Science ouverte | 0,005 | 0,024 |
| Intégrité de la recherche | 0,010 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».