Improving long-term care services: insights from high-reliability organizations
Bibliographic record
Abstract
PURPOSE: Long-term care (LTC) organizations have struggled to protect their vulnerable clients from the ravages of the COVID-19 pandemic. Although various suggestions on containing outbreaks in LTC facilities have gained prominence, ensuring the safety of residents is not just a crisis issue. In that context, the authors must reasses the traditional management practices that were not sufficient for handling unexpected and demanding conditions. The purpose of this paper is to suggest rethinking the underlying attributes of LTC organizations and drawing insight from the parallels they have to high-reliability organizations (HROs). DESIGN/METHODOLOGY/APPROACH: The authors analyzed qualitative data collected from a Canadian LTC facility to shed light on the current state of reliability practices and culture of the LTC industry and to identify the strengths and weaknesses of the traditional management approaches. FINDINGS: To help the LTC industry develop the necessary crisis management capacity to tackle unexpected future challenges, there is an urgent need for adopting a more systemic top-down approach that cultivates mindfulness, learning and resilience. ORIGINALITY/VALUE: This study contributes by applying the HRO theoretical lens in the LTC context. The study provides the LTC leaders with insights into creating a unified effort at the industry level to give rise to a high-reliability-oriented industry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".