Caregiver burden interventions in speech–language pathology: A systematic review
Bibliographic record
Abstract
BACKGROUND: Previous research has demonstrated that many caregivers of care recipients with communication and swallowing impairments suffer from caregiver burden. Existing research sheds light on the presence of burden and various predicting factors, but little information on interventions to reduce caregiver burden. AIMS: To determine how speech-language pathologists (SLPs) address caregiver burden in clinical practice. METHODS: A systematic review was conducted according to PRISMA guidelines and 1898 unique articles were assessed for eligibility from nine electronic databases. Only 11 studies carried out a caregiver burden intervention involving an SLP. Details of the interventions were extracted per the Rehabilitation Treatment Specification System (RTSS) guidelines. MAIN CONTRIBUTION: Results of the review revealed that SLP-led caregiver burden interventions can be effective in reducing burden. Multiple aspects of the intervention approaches, such as multidisciplinary care and targeting emotional burden, are discussed. Demographic factors, such as gender and socio-economic status (SES), are also taken into consideration. CONCLUSIONS & IMPLICATIONS: This review suggests that SLPs can be effective at reducing caregiver burden through interventions involving caregivers across the lifespan and continuum of care. WHAT THIS PAPER ADDS: What is already known on this subject Previous research has demonstrated that many caregivers of care recipients with communication and swallowing impairments suffer from caregiver burden. These caregivers range from parents of young children to spouses of individuals with dementia. However, it is unclear if and how SLPs address caregiver burden in clinical practice. What this paper adds to existing knowledge Using the RTSS, details of various caregiver burden interventions involving SLPs were identified. This review revealed that SLP-led caregiver burden interventions can be effective in reducing emotional and financial burden, as well as play a part in improving care-recipient outcomes. What are the potential or actual clinical implications of this work? This systematic review demonstrates that efforts to reduce caregiver burden within SLP practice may yield benefits for both caregivers and care recipients. It provides clinicians with a preliminary resource to help consider caregiver burden interventions that best suit the needs of the caregivers and their care recipients.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".