Barriers and Benefits of Information Communication Technologies Used by Health Care Aides
Bibliographic record
Abstract
BACKGROUND: Although information and communication technologies (ICT) are becoming more common among health care providers, there is little evidence on how ICT can support health care aides. Health care aides, also known as personal care workers, are unlicensed service providers who encompass the second largest workforce, next to nurses, that provide care to older adults in Canada. OBJECTIVE: The purpose of this literature review is to examine the range and extent of barriers and benefits of ICT used by health care workers to manage and coordinate the care-delivery workflow for their clients. METHODS: We conducted a literature review to examine the range and extent of ICT used by health care aides to manage and coordinate their care delivery, workflow, and activities. We identified 8,958 studies of which 40 were included for descriptive analyses. RESULTS: We distinguished the following five different purposes for the use and implementation of ICT by health care aides: (1) improve everyday work, (2) access electronic health records for home care, (3) facilitate client assessment and care planning, (4) enhance communication, and (5) provide care remotely. We identified 128 barriers and 130 benefits related to adopting ICT. Most of the barriers referred to incomplete hardware and software features, time-consuming ICT adoption, heavy or increased workloads, perceived lack of usefulness of ICT, cost or budget restrictions, security and privacy concerns, and lack of integration with technologies. The benefits for health care aides' adoption of ICT were improvements in communication, support to workflows and processes, improvements in resource planning and health care aides' services, and improvements in access to information and documentation. CONCLUSION: Health care aides are an essential part of the health care system. They provide one-on-one care to their clients in everyday tasks. Despite the scarce information related to health care aides, we identified many benefits of ICT adoption.
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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 | 0.021 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".