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Record W3215457325 · doi:10.5770/cgj.24.514

Position Statement for Mental Health Care in Long-Term Care During COVID-19

2021· article· en· W3215457325 on OpenAlexafffundvenueabout
Claire Checkland, Sophiya Benjamin, Marie‐Andrée Bruneau, Antonia Cappella, Beverley Cassidy, David Conn, Cindy J. Grief, Alvin Keng, Julia Kirkham, P. J. Swathy Krishna, Lisa McMurray, Kiran Rabheru, Marie-France Tourigny-Rivard, Dallas Seitz

Bibliographic record

VenueCanadian Geriatrics Journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsNOSM UniversityUniversity of CalgaryUniversity of TorontoDalhousie UniversityMcMaster UniversityUniversity of AlbertaUniversité de MontréalUniversity of OttawaBaycrest HospitalCanadian Mental Health Association
FundersCanadian Institutes of Health ResearchConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsMedicineMental healthLong-term carePosition statementPandemicCoronavirus disease 2019 (COVID-19)Health careGerontologyGeriatric psychiatryPopulationPsychiatryNursingFamily medicineEnvironmental healthDisease

Abstract

fetched live from OpenAlex

COVID-19 has disproportionately impacted older adults in long-term care (LTC) facilities in Canada. There are opportunities to learn from this crisis and to improve systems of care in order to ensure that older adults in LTC enjoy their right to the highest attainable standard of health. Measures are needed to ensure the mental health of older adults in LTC during COVID-19. The Canadian Academy of Geriatric Psychiatry (CAGP) and Canadian Coalition for Seniors' Mental Health (CCSMH) have developed the following position statements to address the mental health needs of older adults in LTC facilities, their family members, and LTC staff. We outlined eight key considerations related to mental health care in LTC during COVID-19 to optimize the mental health of this vulnerable population during the pandemic.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.000
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 head0.029
GPT teacher head0.390
Teacher spread0.361 · 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 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

Citations13
Published2021
Admission routes4
Has abstractyes

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