Facilitated reflection meetings as a relational approach to problem-solving within long-term care facilities
Bibliographic record
Abstract
Care workers have valuable knowledge to contribute to the improvement of their work environments. Yet incorporating their perspectives into organizational decision-making within long-term care facilities (LTCFs) has been an ongoing challenge. In this article we investigate a promising practice that brought workers and management together in weekly and bimonthly facilitated reflection meetings to identify and resolve problems. Drawing on observations as well as individual and group interviews, we sought to understand whether and how this intervention worked from the perspective of participants. Our study found that one of the main achievements was creating a safe space for workers to speak honestly. They felt heard and treated with respect. In this context, they were willing to surface concerns, failures, and problems for collective deliberation and action. The inclusion of a range of occupational groups ensured that the solutions developed were sensitive to context, including organizational and occupational realities. While the outcomes of the process were impressive, this paper highlights the relational work that created trust, respect, and a spirit of collaboration. We suggest that such facilitated reflection processes may serve as an important strategy to improve the organization of work in LTCFs, one that is particularly well-suited to the dynamic and relational nature of care.
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".