Meaning-making dynamics within and across workgroups : an inquiry into the creation and movement of usable knowledge in a long-term care facility in Ontario
Bibliographic record
Abstract
This dissertation reports the results of a qualitative investigation of the meaning-making patterns that occur in a group of caregivers in a long-term care home. The research design included an ethnographic component to reveal the overall meaning-making dynamic in the site, and a case study focusing on a specific improvement initiative. Data was gathered over a three month period through observation, interviews, and documents. Data analysis included the creation of analytic memos, categorizing and theming, and the development of a meaning-making map to depict systematic interactions. The findings suggest that research participants have developed systemic patterns of meaning making that allow them to create experiences of coherence, purpose, identity, and competence. Eight meaning-making themes emerge from the analysis to reveal a workplace where emphasis is placed on immediate concerns and priorities, and where long-term planning and change are problematic. Caregivers begin each day by creating a coherent picture of their workplace, and they engage in brief, pragmatic interactions throughout the day to maintain their shared understanding of the unfolding context around them. Reflection upon the themes allows for the construction of a meaning-making map, which shows how knowledge of the floor (the residents who live on the floor, the staff who work there, the procedures for carrying out the work, and the physical layout and location of key resources) allows staff to accomplish tasks, and how it simultaneously limits their ability to improve the efficiency and effectiveness of the work and the quality of life of residents. This study confirms previous research and policy reports that describe Canada's long-term care workplaces as highly stressed. The study also supports the contention that strategies to implement a one-way transfer of external knowledge into frontline practices will confront stubborn barriers, and that knowledge moves through processes of exchange--through relationships and interactions--rather than transfer. This inquiry extends the work of organizational researchers and theorists who have attempted to reveal the dynamics of collective learning and sensemaking in workgroups. The practical implications of this study include the importance of using existing interaction patterns as a vehicle for introducing new ideas and practices into long-term care homes, and the advisability of considering whether current long-term care staffing levels are adequate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".