Health-care Workforce Training to Effectively Support Family Caregivers of Seniors in Care
Bibliographic record
Abstract
INTRODUCTION: Family caregivers (FCGs) play an integral, yet often invisible, role in the Canadian health-care system. As the population ages, their presence will become even more essential as they help balance demands on the system and enable community-dwelling seniors to remain so for as long as possible. To preserve their own well-being and capacity to provide ongoing care, FCGs require support to the meet the challenges of their daily caregiving responsibilities. Supporting FCGs results in better care provision to community-dwelling seniors receiving health-care services, as well as enhancing the quality of life for FCGs. Although FCGs rely upon health-care professionals (HCPs) to provide them with support and services, there is a paucity of research pertaining to the type of health workforce training (HWFT) that HCPs should receive to address FCG needs. Programs that train HCPs to engage with, empower, and support FCGs are required. OBJECTIVE: To describe and discuss key findings of a caregiver symposium focused on determining components of HWFT that might better enable HCPs to support FCGs. METHODS: A one-day symposium was held on February 22, 2018 in Edmonton, Alberta, to gather the perspectives of FCGs, HCPs, and stakeholders. Attendees participated in a series of working groups to discuss barriers, facilitators, and recommendations related to HWFT. Proceedings and working group discussions were transcribed, and a qualitative thematic analysis was conducted to identify key themes. RESULTS: Participants identified the following topic areas as being essential to training HCPs in the provision of support for FCGs: understanding the FCG role, communicating with FCGs, partnering with FCGs, fostering FCG resilience, navigating healthcare systems and accessing resources, and enhancing the culture and context of care. CONCLUSIONS: FCGs require more support than is currently being provided by HCPs. Training programs need to specifically address topics identified by participants. These findings will be used to develop HWFT for HCPs.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".