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Enregistrement W6929900566 · doi:10.5064/f6ycw448

Experiences of Serious Illness Conversations (SICs) to Drive Health Equity in Serious Illness Care

2023· dataset· en· W6929900566 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
Aucune affiliation canadienne. Une base fondée sur la seule affiliation (le devis habituel) n'aurait jamais vu ce travail. C'est l'un des travaux qui justifient l'inversion de la base.

Notice bibliographique

RevueSyracuse University Qualitative Data Repository · 2023
Typedataset
Langueen
DomaineDecision Sciences
ThématiqueScientific Computing and Data Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHealth equityEquity (law)PopulationSocioeconomic statusHealth carePerspective (graphical)PandemicSociology of health and illnessPrimary care

Résumé

récupéré en direct d'OpenAlex

<h3>Project Overview</h3> <p>The primary purpose of this study is to describe the experience with and perception about serious illness conversations (SICs) from the perspective of patients from underserved minoritized groups. Secondary goal is to identify structure and contents that make SICs accessible and acceptable by patients from underserved minoritized groups.</p> <p>Evidence demonstrates that SICs, in which clinicians and patients discuss patients’ goals and values, lead to better patient-centered care. However, we also know that both patient participation and effectiveness of SIC may vary dependent on the patient’s background (e.g. race/ethnicity, level of education and health literacy, socioeconomic status, living conditions, ability, or sexual orientation). Variations in patients’ backgrounds may not only hinder their participation in SICs, but also preclude the patients from receiving treatment that aligns with her/his preferences – both of which may result in health inequities.</p> <p>In addition, most patients who have participated in SICs studies have been non-Hispanic White, and effectiveness of the current SIC approach for minority populations is not known. The disproportional impact of COVID-19 pandemic on minority groups raises the urgency to reevaluate and strengthen the SIC approach to engage minority vulnerable population in advance care planning. Therefore, it is critical to examine SIC experiences in depth from the perspectives of patients in this population and gain understanding about the best ways to have SICs with this population, including healthcare professionals best positioned to have SICs with them.</p> <h3>Data Collection Overview</h3> <p>Four primary care clinics and one nursing home which serve diverse inner-city communities in the Pacific Northwest were the study recruitment sites. Clinicians in these sites were asked to identify patients who met our criteria and refer interested patients to the researchers. Researchers contacted the patients by telephone, described the study, confirmed that inclusion criteria were met, and obtained verbal consent for an interview. </p> <p>The data collection method was individual interviews with patients living with a serious illness, receiving care in the study clinics and the nursing home, and are from underserved minority groups and/or vulnerable populations. Of 49 patients referred to the study and contacted, 30 agreed to participate the interview. </p> <p>The PI, Seiko Izumi, conducted all but one of the interviews; Ellen Garcia (MSN, RN, PhD student at Oregon Health & Science University) was a research assistant for this project and conducted interview with participant ID24.</p> <p>For summary participant characteristics, see Table 1 in the Data Narrative. Average interview time was 38 minutes (range 19-89 minutes). Interviews were conducted either in-person, via telephone, or video call (WebEx software) based on the participant’s preference. Interviews were audio-recorded with participants’ permission and later were transcribed verbatim by a HIPAA-compliant transcription. De-identified interview transcripts were imported into ATLAS.ti (qualitative data management and analysis software) for analysis.</p> <p>All participants who gave consent to participate in the interview, also agreed to have their data deposited for research re-use.</p> <h3>Data Analysis</h3> <p>Three investigators (Izumi, Garcia and Andrew Kualaau, PhD student at Oregon Health & Science University) read the transcripts independently and coded texts to capture the experiences of advance care planning. The investigators met regularly to discuss their interpretation of data to reach consensus describing participants’ experiences. External experts in the field of advance care planning with marginalized population reviewed the results of the preliminary analysis and provided feedback to enhance rigor, transferability, and authenticity of our findings.</p> <h3>Selection and Organization of Shared Data</h3> <p>This data project consists of 30 individual interview transcripts, the consent script and interview guide used, a demographic characteristics inventory, a coding groups report, as well as a Data Narrative and an administrative README file. (A handful of sequential numbers in the transcripts – IDs: 17,18,19, 33,34 – do not appear, because they were assigned to referred potential interview candidates, who did not meet the study criteria.)</p> <h3>Other Contributors</h3> <p>Justine Sanders (MD, McGill University) was an expert consult for this study. Renee Henrique (Quality Specialist Providence Health) and Linda DeSitter (Providence Health Director of Palliative Care) served as additional project members.</p>

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.

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)0,008
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Science ouverte
Catégories consensuellesScience ouverte
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,314
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0080,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,004
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0080,011
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,174
Tête enseignante GPT0,472
Écart entre enseignants0,298 · 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