Social determinants of health—to screen or not to screen? How, when, and what to do next are really the questions.
Notice bibliographique
Résumé
169 Background: Social determinants of health (SDOH) have a considerable impact on the health outcomes of chronically ill patients. Although the implementation of social needs screening in clinical settings has been studied, patient perspectives of discussing SDOH with health care providers has not been thoroughly investigated. This study sought to explore the experience and perspectives of limited-resource patients with cancer regarding SDOH discussions. Methods: This cross-sectional analysis used data from a nationwide survey distributed in May 2022 by Patient Advocate Foundation (PAF). The survey was fielded via email to patients who received PAF services in 2020. Inclusion criteria included a valid email, aged >19 and a current or previous cancer treatment. Frequencies and percentages were calculated for categorical variables. Questions focused on individual experiences with SDOH screening, conversations and expectation around information use and assistance. Results: A total of 481 survey respondents with cancer completed the survey. Most respondents were female (73%), aged 56-75 (52%), household income < $48,000 (66%), and insured (98%); 38% were Black, Indigenous or Persons of Color (BIPOC). The most common cancer types were hematologic (42%) and breast (34%); 30% were diagnosed < 2 years prior and 82% received treatment in past 6 months. One quarter (26%) stopped or delayed care in past 12 months due to cost and 66% reported that social needs interfered with treatment in past 12 months. Two-thirds (64%) reported conversations about social needs in the past 12 months. Transportation (36%), food insecurity (32%) and personal safety were the most cited nonmedical needs. Conversations were most often initiated by nurse/PA (30%), social worker (30%) or doctor (29%) and patients reported being ‘extremely comfortable’ being asked these questions by the same providers; doctor (54%), nurse/PA (48%), social worker (46%). Over half (53%) reported comfort with SDOH information being part of their medical record; 61% wanted to be asked SDOH questions face-to-face. If a social need was identified, patients trusted patient advocacy groups (64%), social worker (61%) and charitable non-profit organizations (49%) to help them locate assistance. Only 21% indicated knowledge of availability of needs navigation services. Conclusions: Although patients are open to sharing social issues with providers, our data suggests that conversations may not be routinely initiated in clinical settings. There is also a need to increase awareness of resources in response to SDOH screening. Challenges in trust and privacy persist when disclosing this information. Achieving health equity requires culturally responsive strategies to embed screening and referrals into workflow to ensure cancer patients’ needs are identified and they are linked to appropriate nonmedical resources and services.
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 | 0,005 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».