Geriatrics Today, 5, pp-pp September 2002 GERIATRICS Today: J CAN GERIATR SOC 132 CLINICAL INVESTIGATION STAFF BURNOUT IN LONG-TERM CARE FACILITIES
Bibliographic record
Abstract
Background: A rapidly changing health and long-term care environment characterized by efficiency and cost-contain-ment is resulting in changing roles and responsibilities among all levels of staff who work with seniors in long-term care facilities. More is being asked of all, and there are reports of health-care providers who are overworked, stressed-out and suffering from burnout. Little is known of the burnout experienced by staff in long-term care facilities. We investigated levels of burnout among nursing personnel who provide care to seniors in long-term care facilities in the Ottawa-Carleton Region. Methods: Methods were exploratory and descriptive and employed the use of mail-back questionnaires from a ran-dom and proportional sample of 86 registered nurses (RNs), 92 registered practical nurses (RPNs) and 49 health-care aides (HCAs). The Maslach Burnout Inventory was used to gather data about respondents ’ perceptions of their level of personal accomplishment, emotional exhaustion, involve-ment and depersonalization. Results: Mean scores were highest on measures of per-sonal accomplishment (7.3) and emotional exhaustion (5.7). There were statistically significant differences between HCAs (7.0) and RPNs (5.2) or RNs (5.0) on levels of emo-tional exhaustion. Mean scores were lowest on measures of depersonalization (4.1) and involvement (5.0). HCAs (5.7) differed significantly from either RPNs (4.8) and RNs (4.6) on level of involvement. Conclusion: Staff burnout does not auger well for the pro-vision of high quality care to residents of long-term care facilities. Administrators should strive to reduce staff’s level of emotional exhaustion and increase their level of person-al involvement with residents, to ensure care that is caring and comprehensive. Key words: Staff, burnout, long-term care facilities, frail elderly
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".