Integrating Family-Centered Care Into Chronic Dysphagia Management: A Tutorial
Bibliographic record
Abstract
Purpose: Chronic illnesses and sources of disability, such as dysphagia, are experienced by the entire family system. A narrow focus on impairment-level treatment cannot account for the often-dramatic shift in biopsychosocial functioning that is experienced by both the individual with dysphagia and their family members. To maximize treatment effectiveness, it is therefore necessary to focus more holistically on family resilience and adaptation. Drawing from the principles of family systems theory and the biopsychosocial–spiritual framework, this tutorial explores the application of family-centered care models to chronic dysphagia management. Using a case study to illustrate dysphagia's impact on family caregivers, we suggest a number of opportunities for speech-language pathologists to appropriately engage in family-centered care. Conclusions: Clinicians need to recognize that chronic dysphagia's widespread consequences necessitate the integration of the family into intervention plans. This requires a better understanding of family dynamics, particularly as they relate to the medical condition. As part of the management process, clinicians should consider explicitly inquiring about specific sources of support the family may require and make referrals to other professionals as needed. They should also consider implementing strategies to support the family into intervention in a way that minimizes burden and respects their capabilities. It is only through a more family-centered care approach that clinicians will be able to reduce the impacts of third-party disability and improve the lives of patients and their families.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".