Are healthcare aides underused in long-term care? A cross-sectional study on continuing care facilities in Canada
Bibliographic record
Abstract
OBJECTIVES: Over the last 10 years, appropriate workforce utilisation has been an important discussion among healthcare practitioners and policy-makers. The role of healthcare aides (HCAs) has also expanded to improve their utilisation. This evolving role of HCAs in Canada has prompted calls for standardised training, education and scope of practice for HCAs. The purpose of this research was to examine the differences in HCAs training and utilisation in continuing care facilities. DESIGN: From June 2014 to July 2015, we conducted a mixed-method study on HCA utilisation in continuing care. This paper presents findings gathered solely from the prospective cross-sectional survey of continuing care facilities (long-term care (LTC) and supportive living (SL)) on HCA utilisation. SETTING AND PARTICIPANTS: We conducted this study in a Western Canadian province. The managers of the continuing care facilities (SL and LTC) were eligible to participate in the survey. PRIMARY OUTCOME MEASURES: The pattern of HCAs involvement in medication assistance and other care activities in SL and LTC facilities. RESULTS: We received 130 completed surveys (LTC=64 and SL=52). Our findings showed that approximately 81% of HCAs were fully certified. We found variations in how HCAs were used in SL and LTC facilities. Overall, HCAs in SL were more likely to be involved in medication management such as assisting with inhaled medication and oral medication delivery. A significantly larger proportion of survey respondents from SL facilities reported that medication assistance training was mandatory for their HCAs (86%) compared with the LTC facilities (50%) (p value <0.01). CONCLUSION: The utilisation of HCAs varies widely between SL and LTC facilities. HCAs in SL facilities may be considered better used according to their required educational training and competencies. Expanding the role of HCAs in LTC facilities may lead to a cost-effective and more efficient utilisation of workforce in continuing care facilities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".