Understanding Clinician Perspectives and Actions to Address Caregiver Burden in Caregivers of Adults
Bibliographic record
Abstract
Purpose: The purpose of this study was to determine how speech-language pathologists (SLPs) perceive and address caregiver burden. Method: A 29-question survey was distributed virtually via relevant Facebook pages and professional associations. Ninety-three responses from English-speaking SLPs working with adults in the United States or Canada were analyzed descriptively. Other analyses included one-way analysis of variance and chi-square tests to assess relationships between years of experiences, primary work setting, and other relevant variables, as well as a thematic analysis of one free-text response detailing how respondents defined caregiver burden. Results: Respondents associated caregiver burden with physical, psychological, financial, and social strain, loss of personal time, and the COVID-19 pandemic. The most described theme (80%) was physical strain. Almost all respondents (92%) believed that caregiver burden is a prominent issue and reported using strategies such as education and training to reduce burden. Most respondents (86%) would consider adopting a standardized tool to identify caregiver burden. Common barriers to addressing caregiver burden were lack of tools, time, and expertise. Additionally, less than half of respondents reported being confident in their ability to appropriately refer caregivers for support (45%). Conclusions: Most SLPs interacted with caregivers and used strategies such as education and training within their areas of expertise to try and address caregiver burden. Further research into the resources SLPs need to address caregiver burden is required. This study provides a preliminary understanding of current SLP perspectives and the strategies currently used to address caregiver burden in the United States and Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".