A Grounded Theory of Intensive Care Nurses’ Experiences and Responses to Uncertainty
Bibliographic record
Abstract
The purpose of this study was to develop a theory to explain how nurses experience and respond to uncertainty arising from patient care-related situations and the influence of uncertainty on their information behaviour. Strauss and Corbin’s (1998) grounded theory approach guided the study. Semi-structured face-to-face interviews were conducted with 14 staff nurses working in an adult medical-surgical intensive care unit (MSICU) at one of two participating hospitals. The grounded theory recognizing and responding to uncertainty was developed from constant comparison analysis of transcribed interview data. The theory explicates recognizing, managing, and learning from uncertainty in patient care-related situations. Recognizing uncertainty involved a complex recursive process of assessing, reflecting, questioning and/or predicting, occurring concomitantly with facing uncertain aspects of patient care situations. Together, antecedent conditions and the process of recognizing uncertainty shaped the experience of uncertainty. Two main responses to uncertainty were physiological/affective responses and strategies used to manage uncertainty. Resolved uncertainty, unresolved uncertainty, and learning from uncertainty experiences were three consequences of managing uncertainty. The ten main categories of antecedent, actions and interactions, and consequences that comprised the theory were interrelated and connected through temporal and causal statements of relationship. Nurse, patient, and contextual factors were linked through patterns of conditions and intervening relational statements. Together, these conceptual relationships formed an explanatory theory of how MSICU nurses experienced and responded to uncertainty in their practice. This theory provides understanding of how nurses think through, act and interact in patient situations for which they are uncertain, and provides insight into the nature of the processes involved in recognizing and responding to uncertainty. Study implications for practice, nursing education, and further theory development and research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".