MétaCan
Menu
← Back to cohort
Record W3112251202 · doi:10.1093/geroni/igaa057.597

The Action Project Method Applied in Nursing Home Settings

2020· article· en· W3112251202 on OpenAlexaff
Charlotte Sun Jensen, Andrea Gruneir, Matthias Hoben, Jaclyn Tompalski, Adam Easterbrook, Janice Keefe, Carole A. Estabrooks, Sheila K. Marshall

Bibliographic record

VenueInnovation in Aging · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of British ColumbiaMount Saint Vincent UniversityCentre for Advancing Health OutcomesUniversity of AlbertaWorkers Compensation Board of Alberta
Fundersnot available
KeywordsInterviewConversationNarrativeConversation analysisPsychologyAction (physics)Participant observationSocial psychologyAction researchNarrative inquiryNursingMedical educationApplied psychologyMedicinePedagogyCommunicationSociologyLinguistics

Abstract

fetched live from OpenAlex

Abstract The action-project method (APM), developed in counselling psychology and used in various disciplines, has been shown to be useful for understanding major life transitions in different contexts. We argue that the APM is beneficial for studying the impact of nursing home (NH) home admission and daily life of residents and their families/friends. The APM enables researchers to explore how residents and their families/friends experience NH-life at individual and supraindividual levels of analysis. We applied the APM to solicit the views of residents and individuals close to them to understand their priorities for quality care. The APM data collection consisted of three stages. First, a resident and family member or other caregiver met with the interviewer who initiated a conversation about their experience in the NH. The interviewer then left the room but video-recorded the conversation. Second, the interviewer met with each participant to review the video with each participant offering reflection on the original conversation. These sessions were also recorded. Following transcription and analysis of the conversations, 3 lay-language narratives were created: 1 for each individual and 1 for the pair. Third, participants reviewed their own and the pair’s narrative for additional comments. The APM offers a means to give a voice to NH residents and allows for people to talk about their experiences without the presence of a researcher. By using the APM, researchers can break down individual actions of participants and how these actions come together to form the project of navigating care in NHs.

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)0.092
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.486

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.073
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0060.009
Scholarly communication0.0050.004
Open science0.0040.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.002

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 head0.087
GPT teacher head0.470
Teacher spread0.383 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInnovation in Aging→Same topicGeriatric Care and Nursing Homes→French-language works237,207→