Reconciling Temporalities: A Substantive Explanation of the Origins of Difficulty in the Nurse Patient Encounter
Bibliographic record
Abstract
Nurses describe patients as difficult on a regular basis. Nursing research to date has assumed the existence of this phenomenon. Most studies have listed descriptors of the difficult patient and offered interventions for nurses to use to alter patient behavior. Locating of difficulty within the individual and failure to consider the context of the nurse patient encounter is problematic. The practice of locating difficulty in the individual absolves organizations and society of responsibility to work to change factors that contribute to the construction of difficulty. The purpose of this research was to move beyond a focus on the patient as difficult to examine the context of the nurse patient encounter, and to generate a substantive explanation of the origins of difficulty in the encounter. The grounded theory method was used from a constructivist approach. Data sources included (a) 120 hours of participant observation on a family medicine unit in a hospital in Atlantic Canada; (b) 12 formal interviews with former unit patients; (c) 10 formal interviews with unit nursing staff, and numerous informal interviews with nursing staff during participant observation. Reconciling temporalities was the core category that emerged in this study and was the main concern for both nursing staff and patients. The degree of reconciliation required determined the degree of difficulty in the nurse patient encounter. Knowing the patient minimized difficulty in the nurse patient encounter. Reconciliation of time was inherent in getting to know the patient. When nurses did not have the time, and patients did not feel that care has been delivered in a timely manner the result was temporal incongruency. The greater the disparity between the time needed and the time available the greater the need for reconciliation in the encounter. The factors that contributed to temporal incongruencies, and necessitated reconciliation were: Families, availability of supplies and equipment, who is working, and care space changes. Contextual conditions that influenced the process of reconciliation were: Unit reputation and labor market structure changes. The strategies that nurses used to reconcile temporalities were controlling, working together, managing families, and employing geographies of place/bodies. The context of the delivery of nursing care is an important nursing research frontier.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".