A scoping review to examine health care professionals’ experiences as family caregivers
Bibliographic record
Abstract
BACKGROUND: Health and social care systems must confront the challenge of supporting a growing elderly population and their caregivers. Family caregivers who are healthcare professionals are part of this context, but their caregiving experiences remain unclear. OBJECTIVE: This scoping review explored the experiences of healthcare professionals who are also family caregivers for older adults. METHODOLOGY: A scoping review methodology identified and summarized pertinent studies. Searches were conducted in Medline, Embase, PsycINFO, CINAHL, and AgeLine. We sought articles published from each journals' inception to October 19, 2023. Inclusion criteria were English-language studies about healthcare professionals caring for older adult family members. Diverse research designs were included. Data were extracted and synthesized according to key themes. RESULTS: The review included 19 studies, highlighting four themes. Studies were published between 1994 and 2019, with most studies published before 2017. The overarching theme was "Expectations," where healthcare professional family caregivers faced multifaceted expectations from themselves, their families, and the healthcare system. Expectations also highlights the dual role of participants as both caregivers and healthcare professionals This complex interplay led to a theme related to personal consequences, including stress, guilt, and potential burnout, but also positive aspects of the dual roles. Studies described how dual roles could enhance the quality-of-care healthcare professional caregivers provided to their family members. Finally, support needs were identified, emphasizing the importance of workplace accommodations and support from the healthcare system and peers. CONCLUSIONS: The experiences of healthcare professional family caregivers are shaped by unique expectations, resulting in both positive and negative consequences. The support needs of this group are multifaceted, requiring workplace accommodations and tailored support within the healthcare system. Further research is needed to delve deeper into the nuances of their experiences and develop targeted interventions to alleviate the stress and challenges they face in their dual roles. Understanding the evolving needs of healthcare professional family caregivers over time can inform support strategies along the caregiving trajectory.
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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 | 0.027 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".