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Record W4386407342 · doi:10.1017/s0714980823000417

Understanding the Barriers to and Facilitators of Anxiety Management in Residents of Long-Term Care

2023· article· en· W4386407342 on OpenAlexaff
Kayla Atchison, Ann M. Toohey, Zahinoor Ismail, Zahra Goodarzi

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAnxietyLong-term careNursingAssisted livingPsychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Older adults, 65 years of age and older, living in long-term care (LTC) commonly experience anxiety. This study aimed to understand care providers' perspectives on the barriers to and facilitators of managing anxiety in residents of LTC. Ten semi-structured interviews with care providers in LTC were completed. Framework analysis methods were used to code, thematically analyze, designate codes as barriers or facilitators, and map the codes to the Theoretical Domains Framework. Themes were categorized as acting at the resident, provider, or system level, and were labelled as either barriers to or facilitators of anxiety care. Key barriers to anxiety care at each level were resident cognitive impairment or co-morbidities; lack of staff education, staff treatment uptake and implementation; as well as the care delivery environment and access to resources. There is a need to prioritize measurement-based care for anxiety, have increased access to non-pharmacological treatments, and have a care delivery environment that supports anxiety management to improve the care for anxiety that is delivered to residents.

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)0.006
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 score0.962
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.305
Teacher spread0.271 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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