Training foundation doctors in mental health risk assessment as a tool in the fight against suicide
Notice bibliographique
Résumé
Aims To determine the perceptions of Junior Doctors on whether formal training in risk assessment could help to reduce the number of completed suicides following medical contact. Method Foundation trainees within the Great Western Trust were surveyed using a questionnaire. For those trainees that were not present on the acute hospital site, the same questionnaire was distributed by the postgraduate medical team to all trainees using survey monkey. The survey was left open for four weeks. The total response rate was 57/88 foundation trainees. Simple statistical analysis of the data was performed and outlined below. Result 87% of all the trainees have never done a rotation in psychiatry. 51% of foundation doctors have had between 1-5 patients with suicidal behaviour or ideations admitted under the care of a medical team on which they were the junior doctor and up to 26% have admitted to encountering greater than 10 such patients. Only 37% of foundation trainees who have managed patients with suicidal behaviours admitted to having had any formal training in mental health risk assessment. Foundation trainees report being only somewhat confident in the identifying of factors that make a person high risk of completing suicide. 63% of all foundation trainees would refer any patient who expressed suicidal ideation for formal psychiatric assessment. Majority of the trainees were ‘not so confident’ in their ability to assess a patient's risk of suicide and in offering any help to mitigate this risk. None of the trainees have the intention to pursue psychiatry as a medical specialty and majority (60%) intend to pursue medical specialties. 56% of the trainees felt that training foundation doctors formally to assess patient mental health risk, could reduce the percentage of patients with completed suicide following being seen for non-psychiatric reason. Conclusion The UK Foundation Program is a bridge that occupies that gap between undergraduate medical education and specialty training. It therefore an ideal opportunity for training clinicians in mental health risk assessment as one strategy to help reduce completed suicide following non-psychiatric health contact.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».