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Record W2945031054 · doi:10.1017/s071498081900014x

The Experience of Family Physicians and Home Health Staff Involved in an Intervention to Increase Patient-Related Collaboration

2019· article· fr· W2945031054 on OpenAlexafffundabout
Shannon Berg, Sam Sheps, Sabrina T. Wong, Margaret J. McGregor, Ying C. MacNab

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2019
Typearticle
Languagefr
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaUniversity of AlbertaVancouver Coastal Health
FundersCanadian Institutes of Health Research
KeywordsHumanitiesPolitical sciencePrimary health careArtHealth care

Abstract

fetched live from OpenAlex

Les médecins de famille (MF) et le personnel de soins de santé à domicile (PSD) canadiens rencontrent d'importants obstacles lorsqu'ils doivent collaborer pour la prestation de soins aux patients qu'ils ont en commun. Cette étude à méthodologie mixte visait à évaluer la qualité et la viabilité de l'utilisation de l'audioconférence sécurisée dans une optique d'amélioration de la planification des soins pour ces patients. Les données primaires incluaient les résultats d'un sondage réalisé avant et après l'intervention, ainsi que des entretiens semi-structurés et des groupes de discussion post-intervention. Des méthodes statistiques non paramétriques ont été utilisées pour analyser les résultats du sondage, et les données qualitatives ont fait l'objet d'une analyse thématique de contenu. Les résultats des analyses quantitatives et qualitatives ont ensuite été intégrés afin de faire ressortir les inférences reflétant les approches des MF et du PSD relatives aux obstacles et aux avantages de la planification interdisciplinaire des soins. Les MF et le PSD ont montré que des obstacles structurels limitent leur capacité à collaborer. Le PSD et les MF ont également convenu que les rencontres entre les intervenants des deux services étaient bénéfiques pour les patients et que l'utilisation de l'audioconférence constituait une méthode efficiente de planification collaborative des soins. Les limites comprenaient la petite taille de l'échantillon et la courte période d'intervention, compte tenu de l'ampleur des changements attendus. Canadian family physicians (FPs) and home health staff (HHS) experience significant barriers to patient-related collaboration about patients they share. This mixed-methods study sought to determine the quality and sustainability of secure audio conferencing as a way to increase care planning about shared patients. Primary data sources included pre-and post-study administration of a published survey and post-study semi-structured interviews and focus groups. Non-parametric statistical procedures were used to analyze survey results and thematic content analysis was undertaken for qualitative data. Results from both quantitative and qualitative analysis were integrated into the overall analysis, in order to draw inferences reflecting both approaches to barriers and benefits of collaborative care planning for FPs and HHS. Both FPs and HHS provided evidence that structural barriers impede their ability to collaborate. HHS and FPs also agreed that joint conferences were beneficial for patients, and that the use of audio conferencing provided an efficient method of collaborative care planning. Limitations included a small sample size and short timeline for the intervention period, given the magnitude of the expected change.

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)0.022
metaresearch head score (Gemma)0.069
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 score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0040.003
Open science0.0020.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.313
Teacher spread0.300 · 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

Citations3
Published2019
Admission routes3
Has abstractyes

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