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Record W4377099281 · doi:10.1024/1662-9647/a000314

Caring Through COVID

2023· article· en· W4377099281 on OpenAlexaff
Evan Plys, Nina Ahmad, Allegra Netten, Kadija N. Williams, Caitlin J. Tyrrell, Rachel Weiskittle, Shauna Reddin, Milann Mitchell, Jessica Strong

Bibliographic record

VenueGeroPsych · 2023
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicPsychology2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Group psychotherapyPsychotherapistClinical psychologyMedicineGerontologyDisease

Abstract

fetched live from OpenAlex

Abstract: This multisite quality improvement (QI) project reports on a psychotherapy group for family care partners of persons living with neurodegenerative conditions. Following the plan-do-study-act model, a team of geropsychologists iteratively developed, implemented, and refined the 8-week “Caring Through COVID” psychotherapy group across five cycles from January 2021 to April 2022. Participants were 21 spouses or adult children of persons living with neurodegenerative conditions. Across two clinics, participants evidenced moderate improvements in caregiver burden ( d = .59), self-efficacy for caregiving ( d = −.64), and self-efficacy for emotion regulation ( d = −.60). The group was perceived positively by participants. This QI project demonstrates the real-world implementation of a psychotherapy group developed during the COVID-19 pandemic and refined to remain ongoing.

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)0.008
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0020.012
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0180.002

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 head0.158
GPT teacher head0.448
Teacher spread0.289 · 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

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