MétaCan
Menu
Back to cohort
Record W4361291071 · doi:10.1186/s12904-023-01142-3

Perspectives across Canada about implementing a palliative approach in long-term care during COVID-19

2023· article· en· W4361291071 on OpenAlexafffundabout
Julia Kruizinga, Stephanie Pedrotti Lucchese, Shirin Vellani, Vanessa Maradiaga Rivas, Sandy Shamon, Karine Diedrich, Laurel Gillespie, Sharon Kaasalainen

Bibliographic record

VenueBMC Palliative Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsCanadian Hospice Palliative Care AssociationMcMaster University
FundersHealth Canada
KeywordsPalliative careAnticipation (artificial intelligence)Coronavirus disease 2019 (COVID-19)PandemicLong-term careAdvance care planningNursingMedicineQualitative researchFocus groupFamily caregivers2019-20 coronavirus outbreakPsychologyFamily medicineSociologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Long-term care (LTC) homes have been disproportionately impacted during COVID-19. PURPOSE: To explore the perspectives of stakeholders across Canada around implementing a palliative approach in LTC home during COVID-19. METHODS: Qualitative, descriptive design using one-to-one or paired semi-structured interviews. RESULTS: Four themes were identified: (1) the influence of the pandemic on implementing a palliative approach, (2) families are an essential part of implementing a palliative approach, (3) prioritizing advance care planning (ACP) and goals of care (GoC) discussions in anticipation of the overload of deaths and (4) COVID-19 highlighting the need for a palliative approach as well as several subthemes. CONCLUSION: The COVID-19 pandemic influenced the implementation of a palliative approach to care, where many LTC homes faced an overwhelming number of deaths and restricted the presence of family members. A more concentrated focus on home-wide ACP and GoC conversations and the need for a palliative approach to care in LTC were identified.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.450
Teacher spread0.367 · 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 teacher head, not a consensus.

Study designObservational
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
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueBMC Palliative CareSame topicGeriatric Care and Nursing HomesFrench-language works237,207