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Record W4414089023 · doi:10.1136/spcare-2025-acp.12

1239 ‘I never thought I’d have to stand there and make that decision.’ Journey-mapping caregivers’ decisions

2025· article· en· W4414089023 on OpenAlexaffabout
Jessica Simon, Seema King, Sam Hester, Gwenn Boryski, Daniel Gaetano, Maria Santana, Lorraine Venturato, Jasneet Parmar, Jayna Holroyd‐Leduc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsCanadian Patient Safety InstituteUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsDementiaThematic analysisFocus groupQualitative researchHealth careCoding (social sciences)Axial coding

Abstract

fetched live from OpenAlex

Aim To map the decision-making journeys of caregivers of people living with dementia, as a guide towards better caregiver support. Background Family caregivers have a crucial role as substitute decision makers, tasked with honouring personal and medical wishes for persons living with dementia (PLwD). However, they can feel decisional uncertainty, unsupported, unrecognized as partners-in-care, and unaware of dementia trajectories. Methods Caregivers and community organizations in Alberta, Canada were engaged as study team partners throughout the project. A qualitative study was undertaken to map caregivers’ decision-making journeys. Semi-structured, one-to-one interviews were conducted with 25 caregivers recruited through community newsletters. We utilized graphic recording of the interviews for thematic analysis, alongside researcher line-by-line coding of verbatim transcripts, to develop a summary map of caregivers’ journeys in decision-making and decision-supports. This was then shared and refined through 3 focus groups (9 participants) and presented at a World Café with 5 caregivers and 20 healthcare providers and dementia organization stakeholders to explore implementation strategies. Results Caregivers of PLWD struggle with decisions in seven-key areas. The journey map developed from their experiences depicts these as : ‘Getting a diagnosis’, ‘Financial, legal and understanding your person’s wishes’, ‘Driving’, ‘ Day-to-day decisions’, Taking care of yourself, ‘Where to live?’ and ‘End of life Care Decisions,’ Caregivers often described themselves as being ‘lucky’ to happen upon information to help them make decisions through word-of-mouth or through their own advocating. Areas of support needed include emotional support, guidance on ethical decisions, knowing which future decisions to prepare for and finding resources. Available local resources were added to each decision map. Discussion Caregivers are struggling to make decisions for their PLwD. Our map of a caregiver’s journey in decision-making illustrates common decisions points and provides emotional, practical and informational content to support them. Unique Contribution Graphic recording is a novel, accessible method for sharing research data with patient partners and stakeholders. Iterative development of a generic journey map from participants’ varied experiences created an intuitive, low text burden resource for caregivers to use alone, together with PLwD, or other family and healthcare providers. Implications ‘Decision Roadmaps’ can provide an engaging, rapidly accessible, visual overview of the broad advance care planning decisions people may face. This dementia-specific map displays locally available resources. Stakeholder organizations have integrated the map into their services and used it to identify gaps in the times and settings when caregivers most need support.

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)0.003
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: none
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.004

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 head0.031
GPT teacher head0.317
Teacher spread0.286 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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