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Enregistrement W141957184 · doi:10.1177/070674371305800801

Depression in Primary Care: What More Do We Need to Know?

2013· letter· en· W141957184 sur OpenAlexvenueaboutno aff
Tony Kendrick

Notice bibliographique

RevueThe Canadian Journal of Psychiatry · 2013
Typeletter
Langueen
DomainePsychology
ThématiqueMental Health Treatment and Access
Établissements canadiensnon disponible
Organismes subventionnairesNational Institutes of HealthNational Institute for Health and Care Research
Mots-clésDepression (economics)Primary careMedicineQuality of life (healthcare)Management of depressionCohortPsychiatryPsychologyMEDLINEFamily medicineGerontologyNursingPolitical science

Résumé

récupéré en direct d'OpenAlex

AbbreviationsAD antidepressantFP family physicianGP general practitionerIAPT Increasing Access to Psychological TherapiesMDD major depressive disorderQOF Quality and Outcomes FrameworkIn this edition of The Canadian Journal of Psychiatry, Dr Marilyn A Craven and Dr Roger Bland' cite studies suggesting that around 10% of primary care patients are likely to meet diagnostic criteria for MDD, and that numbers will rise as the baby boomer cohort ages and the prevalence of chronic physical disease increases. They suggest that the persistently low rates of detection, treatment, and follow-up found in primary care need addressing to improve treatment adherence and patient outcomes, and that newer evidencebased models of case management and collaborative care need to be adopted, integrating care for depression with that for physical diseases.1Alongside this very useful overview of the issues, Dr Linda Gask2 reviews studies of educating FPs about the identification and treatment of depression, and points out that simple education has largely failed to change practice. She identifies perceived structural obstacles to change, including a relative lack of time and resources in primary care, but highlights a tendency among FPs to conceptualize depression as reactive or endogenous, with subsequent uncertainty about treating it in the face of adverse life events and difficulties.2 This implies that FPs perceive limits to the medical model of depression inherent in the research described by Dr Craven and Dr Bland,1 and the need to take social factors into account, but that they are uncertain about how to do so in practice.We know that the onset of depression is often provoked by adverse social circumstances,3·4 and that the prevalence of depression differs markedly between populations, in accordance with rates of social adversity.5 Cross-sectional surveys using consistent diagnostic criteria suggest that the prevalence of MDD doubled among US adults between 1992 and 2002,6 and all high-income countries saw yearon-year increases in AD prescribing in primary care in the 1990s following the introduction of the selective serotonin reuptake inhibitors,7 prompting talk of a depression epidemic, although we found increased AD prescribing in the United Kingdom to be due to increases in the proportion of sufferers being put on long-term treatment, rather than to a rise in the incidence of depression.7 Rates of consulting for depression actually seemed to be falling during the period of relative affluence in the United Kingdom from 2000 onwards,7-8 at least up until the economic crash in 2008.8 FPs may well question the extent to which they can ameliorate the effects of changes in their patients' financial security, employment, and housing. Anderson et al9 pointed out 20 years ago that the prevalence of case-level psychological distress in a population correlates highly with the mean population level of psychological distress, indicating for them thatThe mental health of society is integral and reflects its social economic and political structure. At this point psychiatric epidemiology and prevention merge into social policy-they cannot exist apart.9·p 484Therefore, interventions are most likely to be effective if they affect the whole of the population rather than the highrisk tail. However, despite this, professionals faced with people in distress must do the best they can for them, even if political solutions may seem to be more likely to make a difference at the population level.The extent to which the recognition of depression by FPs needs to be improved has been questioned, and the old notion that FPs miss 50% or more of cases may be doing them a disservice. Studies have suggested that missed cases tend to be milder,10· and that the recognition of moderateto-severe depression, where the evidence of benefit from treatment is stronger, is actually quite good. In the World Health Organization naturalistic study, in 15 cities around the world, patients whose depression went unrecognized had milder depression at baseline and were not found to have worse outcomes than those recognized. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,014
score de la tête « metaresearch » (Gemma)0,058
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,053
Score d'incertitude au seuil0,104

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,058
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,002
Bibliométrie0,0020,003
Études des sciences et des technologies0,0040,005
Communication savante0,0100,020
Science ouverte0,0030,004
Intégrité de la recherche0,0140,018
Charge utile insuffisante (le modèle a refusé de juger)0,0110,002

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,300
Écart entre enseignants0,284 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2013
Routes d'admission2
Résumé présentoui

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