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Enregistrement W4389233951 · doi:10.1182/blood-2023-177767

Are Medical Learners Adept at Recognizing Heavy Vaginal Bleeding?

2023· article· en· W4389233951 sur OpenAlexaff
Fartoon M. Siad, Filomena Meffe, Andrea Lausman, Carolyn Snider, Martina Trinkaus, Michelle Sholzberg

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueFemale Genital Mutilation/Cutting Issues
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésFeelingFamily medicineMedicineObstetrics and gynaecologyDescriptive statisticsPsychologyMedical educationPregnancy

Résumé

récupéré en direct d'OpenAlex

Background - More than half of reproductive-age females experience heavy vaginal blood loss (VBL), whether menstrual, lochial, or otherwise, and yet less than 10% seek medical attention for evaluation. Moreover, there is evidence that even when females present to medical attention that heavy VBL is serially under-recognized and untreated. Complications from heavy VBL adversely impact health-related quality of life. This makes characterizing and quantifying VBL critical. It is well documented that there are widespread knowledge and care gaps surrounding VBL due to structural multidimensional stigma. Importantly, it is unclear the extent to which medical learners confidently recognize symptoms and signs of heavy VBL. Objectives - To explore the understanding, attitudes, and perceptions of medical learners in characterizing VBL using an online survey. Methods - An online survey was launched to assess trainees' ability to identify heavy VBL. Survey questions were informed by the literature and tested for validity. The survey was distributed online to trainees working at all local University affiliated hospitals. Results were analyzed using descriptive statistics. Institutional ethics approval was obtained. Results - 73 learners (medical students, residents, and fellows) completed the survey. 88% of learners were between the ages of 25-34, and 56% self-identified as women. 27% were 3rd /4th level medical students, 30% were 2nd year residents, 43% 4th year residents or higher. 27% of residents were from Emergency Medicine, 14% Hematology, 12% Family Medicine, 10% Internal Medicine, and 10% Obstetrics and Gynecology. 94% of trainees reported asking about heavy VBL in the last 6 months. Most described feeling comfortable asking about and diagnosing VBL regardless of cultural, religious or ethnic background, sexual orientation, or gender. 56% were aware of bleeding assessment tools (BATs), 51% of menstrual cups, 21% of the pictorial bleeding assessment chart (PBAC) however, less than 60% had used BATs, less than 75% had used menstrual cups and less than 80% had used the PBAC in clinical practice (Figure 1). Overall, trainees acknowledged stigma surrounding iron deficiency without anemia, and that iron deficiency was associated with decreased health-related quality of life. They recognized the need to screen and not rely on patients being forthcoming about excessive VBL. Discussion - Heavy VBL is exceedingly common, has important clinical and psychosocial ramifications, yet it continues to be stigmatized, underdiagnosed, and thus poorly treated. We found that while a broad range of medical learners described themselves as largely feeling comfortable with their skills in assessing VBL and being aware of tools to facilitate the diagnosis of heavy VBL, surprisingly few had used these tools in clinical practice. Our findings highlight important knowledge gaps surrounding VBL and interestingly, show excessive confidence in its diagnosis amongst medical learners. Targeted knowledge translation rooted in theory- and evidence-based implementation science is urgently required in this space. Next steps involve assessing trainee skills in practice and exploring patient lived experiences with VBL through qualitative interviews.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,118
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,002

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 Opus0,043
Tête enseignante GPT0,306
Écart entre enseignants0,263 · 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 tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations2
Publié2023
Routes d'admission1
Résumé présentoui

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