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Record W4391650236 · doi:10.1017/s0714980823000764

Employee Experiences Providing Nutritional Care during the COVID-19 Pandemic

2024· article· en· W4391650236 on OpenAlexaffabout
Heather Alford, Paulette V. Hunter, Allison Cammer

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsThematic analysisPandemicSocializationReflexivityCentralityNursingQuality (philosophy)Long-term carePsychologyHealth careCoronavirus disease 2019 (COVID-19)MedicineQualitative researchSociologyPolitical scienceSocial psychologyDisease

Abstract

fetched live from OpenAlex

Nutritional care is a critical, yet often overlooked component of quality care in long-term care (LTC) that is linked to culture, socialization, and residents' psychological and physiological well-being. Given that several COVID-19 infection control protocols affected nutritional care, this study aimed to understand employees' experiences of these changes. Seven semi-structured interviews were conducted with Saskatchewan healthcare employees from several disciplines, all of whom had a role in supporting nutritional care in LTC. The resulting interview transcripts were analyzed using reflexive thematic analysis. Three main themes characterized the interviewees' reflections: regression to an institutional mealtime environment, unrealistic expectations, and concern for residents. Given the centrality of nutritional care to quality of life, strategies tailored to support staff in providing relationship-centered nutritional care must be further articulated to maintain standards of care for LTC residents in future outbreaks and epidemics.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.004
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.002
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 head0.036
GPT teacher head0.326
Teacher spread0.290 · 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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissement→Same topicGeriatric Care and Nursing Homes→French-language works237,207→