Strategies Used by Home Health Care Professionals Working With Older Adults to Navigate the Institutional Context: An Integrative Review
Bibliographic record
Abstract
To respond to the needs of home healthcare (HHC) patients, HHC professionals must use strategies to navigate the influence of the institutional context, that is, laws and regulations, the administration, and the organization of HHC services. However, no synthesis of those strategies exists. This review aimed to synthesize the strategies used by HHC professionals working with older adults to navigate the institutional context. An integrative review was undertaken in 5 databases, from 2011 to January 2023. The quality of documents was assessed based on an adapted version of the Critical Review Form—Qualitative Studies (Version 2.0) in which a score was calculated out of 25, and data was analyzed through coding, data display and comparison. Thirteen documents were included. The quality of studies ranged from 13 to 21.75. Strategies are often used to overcome limited resources (e.g., time, funding). Six types of strategies were identified: Deviating (bypassing rules or processes), taking on more and more (taking additional work), offering one’s personal time (working without remuneration), reallocating resources (splitting HHC services between patients), limiting HHC visits (restricting interventions or actions) and relying on others (transferring responsibilities). The use of strategies could alleviate the discomfort felt by HHC professionals due to limited resources. However, as some strategies lead to a reduced scope of practice and to a loss of expertise, this could impede the quality of the care, resulting in non-responded needs for HHC patients.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".