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Record W2907140840 · doi:10.1017/s071498081800034x

Factors for Self-Managing Care Following Older Adults’ Discharge from the Emergency Department: A Qualitative Study

2018· article· fr· W2907140840 on OpenAlexaff
Sharon Marr, Loretta M. Hillier, Diane Simpson, Sigrid Vinson, Sarah Goodwill, David Jewell, Afeez Abiola Hazzan

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2018
Typearticle
Languagefr
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsJuravinski HospitalHamilton Health SciencesMcMaster UniversitySt. Peter's Hospital
Fundersnot available
KeywordsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

Cette étude avait pour but d'identifier les facteurs qui influent sur la capacité des personnes âgées à prendre en charge leur santé après une consultation au service des urgences (SU). Les questionnaires de l'enquête (n = 380) ont été remplis en SU par des personnes âgées et leurs aidants et visaient à évaluer leur perception de la compréhension de l'information qui leur était fournie. Des entrevues (n = 51) ont été réalisées avec un sous-échantillon de participants au cours des quatre semaines suivant leur consultation au SU et ont examiné les facteurs ayant une incidence sur l'autogestion des problèmes de santé. La perception de la compréhension de l'information reçue en SU (« oui, certainement ») était meilleure lors de la consultation au SU (91 %) que lors du suivi (71 %), lorsque 20 % des participants ne comprenaient pas ou n'étaient pas certains qu'ils avaient compris ce qui leur avait été communiqué en SU. Les patients ont rapporté que l'autogestion de leurs problèmes de santé était influencée par: la communication avec le personnel du SU, la compréhension des attentes suivant le congé de l'hôpital, l'état de santé, la disponibilité des aidants et divers facteurs externes. De plus, les soignants ont aussi mentionné l'appui aux soignants et la résistance des patients aux recommandations. L'utilisation de stratégies adaptées aux aînés en SU (p. ex. recommandations écrites, confirmation de la compréhension des recommandations), particulièrement celles liées à l'identification des personnes à risque et de celles nécessitant davantage de soutiens transitoires ou un meilleur accès ou intégration aux ressources disponibles dans la communauté amélioreraient l'autogestion des problèmes de santé suivant les consultations en SU. This study identified factors affecting seniors’ ability to self-manage their health following an Emergency Department (ED) visit. Surveys (n = 380) completed by older adults and their caregivers in the ED assessed their understanding of information provided. Interviews (n = 51) completed with a participant subsample up to four weeks post-ED visit examined self-management factors. Perceived understanding of the information (“Yes, definitely”) received in the ED was greater at the time of the visit (91%) than at follow-up (71%). Patients reported self-management was influenced by communication with ED staff, understanding of post-discharge expectations and the health condition(s), caregiver availability, and various external factors. Caregivers also identified support for caregivers and patient resistance to recommendations. Senior-friendly strategies (e.g., recommendations in writing, confirmed understanding of recommendations), particularly those related to identifying those at risk and needing greater transitional supports, and greater access to and integration with community supports could enhance post-ED self-management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.333
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2018
Admission routes1
Has abstractyes

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