Orchestrating care : nursing practice with hospitalized older adults
Bibliographic record
Abstract
The majority of recipients of nursing care in Canadian hospitals are older adults; however, research about nurses’ knowledge of aging, beliefs about aging, and institutional contexts and their influence on nursing care practice with older adults remains limited. In this study, grounded theory methods, guided by symbolic interactionism, were used to explore nursing practice with hospitalized older adults. The theory orchestrating care was developed after analysis of 375 hours of participant observations and 35 interviews with 24 participants. The theory of orchestrating care explains how nurses are continuously trying to manage their work environment by understanding the status of the patients on their unit, mobilizing the assistance of others, and stretching available resources to resolve their problem of providing their patients with what they perceived as “good care” while sustaining themselves as “good” nurses in their practice that they described as hard, misunderstood, and under-resourced. They did this through the two subprocesses of building synergy and minimizing strain. Building synergy explains how nurses leverage and share information and gain the assistance of others. Minimizing strain explains how nurses use available resources, support and guide one another, and reframe their practices in ways that create a supportive nursing network. Nurses looked for allies as they developed their lines of action to resolve their problem in work environments they characterized as problematic. When they did not regard other care providers and leaders as allies, nurses focused on their top priority of safety and turned inward for support from other nurses in the hope of relieving their feelings of being overwhelmed, pressured, ignored, and misunderstood. Turning inward to resolve their problem both aided the nurses (by providing short-term relief) and inhibited them (by increasing their isolation). It also prevented them from articulating their challenges to their managers, from building synergy with other healthcare professionals, and from viewing their nursing team differently. Care of hospitalized older adults can be improved by listening to nurses who are working with this population, examining the hospital systems that constrain these nurses’ practice and from nurses critically reflecting on how their practices may be contributing to their challenges.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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 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".