Staff perceptions of pod nursing on an acute mental health unit
Bibliographic record
Abstract
Objective: The Niagara Health System opened a new healthcare facility in 2013 with consolidation of inpatient mental health services at the new hospital site. The model of nursing care delivery was changed from primary nursing to pod nursing on one of the acute mental health units to better align care delivery with the physical lay-out of the unit that created challenges around long-standing care processes such as observational rounds, transfer of accountability, and team communication. Inefficient processes can decrease nursing visibility and accessibility and this can affect patient engagement and unit safety. The study sought to elicit staff perceptions of the impact of the model change for communication and gained efficiencies in completion of observational rounds and transfer of accountability. There is a gap in the literature related to pod nursing and this paper will inform decisions related to use of this model in acute mental health inpatient settings. Methods: A seven-item questionnaire constructed by the authors was sent out by e-mail to the unit’s 22 regular nursing staff nine months after implementation of the pod nursing model. The questionnaire was designed to elicit staff perceptions of the impact of the model change. The analytic sample consisted of results from 13 nursing staff who completed the questionnaire. Descriptive statistics were used to describe perception responses. Results: Results of the survey identified overall staff agreement with pod nursing as an efficient model of care for the unit (92%). The majority (84.6%) of staff agreed that observational rounds were more manageable, the transfer of accountability process works well (100%), and there is improved communication with their colleagues (61.5%). Conclusions: Pod nursing can be effectively implemented in the acute mental health setting and based on streamlined processes it can free up valuable nursing time to be visible and accessible on the unit, which can contribute to a safe and therapeutic milieu.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".