An interprofessional team approach to fall prevention for older home care clients ‘at risk’ of falling: health care providers share their experiences
Bibliographic record
Abstract
BACKGROUND: Providing care for older home care clients 'at risk' of falling requires the services of many health care providers due to predisposing chronic, complex conditions. One strategy to ensure that quality care is delivered is described in the integrated care literature; interprofessional collaboration. Engaging in an interprofessional team approach to fall prevention for this group of clients seems to make sense. However, whether or not this approach is feasible and realistic is not well described in the literature. As well, little is known about how teams function in the community when an interprofessional approach is engaged in. The barriers and facilitators of such an approach are also not known. PURPOSE: The purpose of this qualitative study was to describe the experiences of five different health care professionals as they participated in an interprofessional team approach to care for the frail older adult living at home and at risk of falling. METHODOLOGY: This study took place in Hamilton, ON, Canada and was part of a randomized controlled trial, the aim of which was to determine the effects and costs of a multifactorial and interdisciplinary team approach to fall prevention for older home care clients 'at risk' of falling. The current study utilized an exploratory descriptive design to answer the following research questions: how do interprofessional teams describe their experiences when involved in a research intervention requiring collaboration for a 9-month period of time? What are the barriers and facilitators to teamwork? Four focus groups were conducted with the care-provider teams (n=9) 6 and 9 months following group formation. RESULTS: This study revealed several themes which included, team capacity, practitioner competencies, perceived outcomes, support and time. Overall, care providers were positive about their experiences and felt that through an interprofessional approach benefits could be experienced by both the provider and the patient and his/her family. Findings from this study suggest that research needs to be conducted to further explore the issues faced by this group of care providers and potential client outcomes.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".