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Record W4294897844 · doi:10.1177/16094069221125054

The Action Project Method Applied in Nursing Home Settings

2022· article· en· W4294897844 on OpenAlexafffund
Charlotte Jensen, Matthias Hoben, Stephanie Chamberlain, Sheila K. Marshall, Adam Easterbrook, Andrea Gruneir

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British ColumbiaUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsData collectionAction (physics)NursingFocus groupAction researchQualitative researchQualitative propertyPsychologyHealth careMedicineComputer scienceSociologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

The Action-Project Method (A-PM) is a qualitative research approach used to understand the actions and experiences co-constructed by individuals. We applied the A-PM in a nursing home (NH) setting with the aim to explore how NH residents and the people closest to them describe their priorities for care and act on these priorities. Due to the health of residents, the demands on staff and family members, and general issues around scheduling, applying the A-PM in the setting required adaptations. The core focus of this article is on the necessary adaptations to apply the A-PM in a NH setting. (1) The A-PM is typically longitudinal with multiple data collection cycles; however, given the circumstances surrounding the residents’ health and the highly structured institutional setting, we opted for a single round of data collection. (2) During recruitment, three residents asked to participate alone, which we accommodated to acknowledge their experiences. (3) The setting posed challenges to data collection such as ensuring privacy and avoiding interruptions, but these challenges often reinforced participants’ experiences. With the necessary adaptations the A-PM in NHs gives voice to participants while contextualizing that within their core relationships.

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)0.029
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.542
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.486
GPT teacher head0.708
Teacher spread0.222 · 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
GenreMethods

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

Citations2
Published2022
Admission routes2
Has abstractyes

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