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Record W4412907105 · doi:10.3390/healthcare13151899

Bridging the Care Gap: Integrating Family Caregiver Partnerships into Healthcare Provider Education

2025· article· en· W4412907105 on OpenAlexafffundabout
Jasneet Parmar, Tanya L’Heureux, Sharon Anderson, Michelle Lobchuk, Lesley Charles, Cheryl Pollard, Linda Powell, Esha Ray Chaudhuri, Joelle Fawcett-Arsenault, Sarah Mosaico, Cindy Sim, Kimberly Shapkin, Carolyn Weir, L Sproule, Megan Strickfaden, Glenda Tarnowski, Jonathan W. Lee, Cheryl Cameron

Bibliographic record

VenueHealthcare · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsAir CanadaNorQuest CollegeCARE CanadaUniversity of CalgaryUniversity of ReginaUniversity of ManitobaUniversity of Alberta
FundersAlberta HealthUniversity of Alberta
KeywordsChampionWorkforcePsychologyNursingService providerHealth careMedical educationWorkforce developmentCore competencyKnowledge managementService (business)MedicineBusinessComputer science

Abstract

fetched live from OpenAlex

Background: Family caregivers are a vital yet often under-recognized part of the healthcare system. They provide essential emotional, physical, and logistical support to individuals with illness, disability, or frailty, and their contributions improve continuity of care and reduce system strain. However, many healthcare and social service providers are not equipped to meaningfully engage caregivers as partners. In Alberta, stakeholders validated the Caregiver-Centered Care Competency Framework and identified the need for a three-tiered education model—Foundational, Advanced, and Champion—to help providers recognize, include, and support family caregivers across care settings. This paper focuses on the development and early evaluation of the Advanced Caregiver-Centered Care Education modules, designed to enhance the knowledge and skills of providers with more experience working with family caregivers. The modules emphasize how partnering with caregivers benefits not only the person receiving care but also improves provider effectiveness and supports better system outcomes. Methods: The modules were co-designed with a 154-member interdisciplinary team and grounded in the competency framework. Evaluation used the first three levels of the Kirkpatrick–Barr health workforce education model. We analyzed pre- and post-surveys from the first 50 learners in each module using paired t-tests and examined qualitative feedback and SMART goals through inductive content analysis. Results: Learners reported a high level of satisfaction with the education delivery and the knowledge and skill acquisition. Statistically significant improvements were observed in 53 of 54 pre-post items. SMART goals reflected intended practice changes across all six competency domains, indicating learners saw value in engaging caregivers as partners. Conclusions: The Advanced Caregiver-Centered Care education improved providers’ confidence, knowledge, and skills to work in partnership with family caregivers. Future research will explore whether these improvements translate into real-world practice changes and better caregiver experiences in care planning, communication, and navigation.

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)0.020
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.034
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.006
Open science0.0030.015
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.074
GPT teacher head0.477
Teacher spread0.404 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2025
Admission routes3
Has abstractyes

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