Caregiver-Centered Care Health Workforce Competencies: Developing Consistent Support for Family Caregivers
Bibliographic record
Abstract
Abstract Family caregivers [FCGs] are the backbone of the health system. They provide over 80% of the care for people with dementia, chronic illnesses and impairments. Despite evidence of their contributions and consequences of caregiving, support for FCGs has not been a health system priority. Education to prepare health providers to effectively identify, engage, assess, and support FCGs throughout the care trajectory is an innovative approach in addressing inconsistent system of supports for FCGs. We report on development and validation of the Caregiver-Centered Care Competency Framework to help with curricular design and subsequent evaluation of effectiveness of care providers working within healthcare settings to engage and support FCGs. We used a three round modified Delphi approach. An expert panel of 42 international, national, and provincial stakeholders agreed to participate. In the first 2 rounds, multi-level, interdisciplinary participants, rated the indicators in terms of importance and relevance. In the 3rd round consensus meeting, participants validated the six competency domains, including indicators in small group sessions. Thirty-four experts (81%) participated in the round 1, 36 (85.7%) in round 2, and 42 people (100%) in round 3. There was stable consensus across all three rounds, 96.07% of participants rated the indicators as essential or important (Round 1, 95.81%; Round 2, 94.15; Round 3, 98.23%). FCG research has been primarily focussed on educating FCGs to provide care. These competencies will shape the design of educational curricula and interdisciplinary training programs aimed at supporting the health and social care workforce to provide caregiver-centered care.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".