Attitudes and attributions of Winnipeg emergency nurses : a correlational self-report survey
Bibliographic record
Abstract
The emergency department is a major point of entry into the health care system for increasing numbers. This increasing demand strains available resources, necessitating a system of priority setting. Ideally, assessment and disposition of patients should be based on the severity of presenting signs and symptoms, but this may not always be the case. Despite organizational philosophies and policies to the contrary, emergency staff make attributions concerning patients and the "legitimacy" of patients regarding their services. Attribution making is the cognitive process of ascribing meaning and characteristics to behaviour, taking into account the perceiver's experiences, motivations, emotions, and beliefs. Attributions then mediate emotional reactions, attitudes, and behaviours within the perceiver, which can have significant implications for the care nurses provide. The study was guided by an attribution based framework, entitled Models of Helping and Coping (Brickman, Rabinowitz, Karuza, Coates, Cohn & Kidder, 1982). The purpose of the study was to examine one consequence of attribution making, attitudes. The research question was threefold: (1) What attributions do emergency nurses make about emergency patients regarding responsibility for cause of problems and responsibility for solution of problems? (2) What is the attitude emergency nurses hold toward patients, especially those classified as psychiatric emergencies? (3) What is the correlation between the attributions of responsibility for cause and solutions, and admitted attitudes toward patients classified as psychiatric emergencies? The research design was a correlational, self-report survey. Data collection was carried out via three self-report questionaires, which included: 1) the Emergency Vignettes Questionaire; 2) the Attitudes Towards Patients Survey (ATP) (Roskin, Carsen Rabiner & Lenon, 1986); and 3) a demographic data questionaire. The sample consisted of 102 registered nurses, who worked with adult patients, within the emergency department of the seven Winnipeg acute care facilities. Descriptive, comparison, and correlational statistics were used to analyze data. Nurses' attributions of responsibility for cause and solution to each vignette were examined. The ATP Survey had seven subscales that measured beliefs regarding the etiology of patients' illnesses and attitudes toward the helper/client relationship. Analysis also involved determining the degree and type of correlation between the attribution subscales and attitude subscales. The effect of sample characteristics (gender, age, years of nursing, years of emergency nursing, practice setting, work status, and education) was tested using t-tests. Results of the Emergency Vignette Questionaire showed that nurses generally preferred the Medical Model, which assigns low responsibility for cause and solution of a problem to the patient. The second most frequently selected model was the Enlightenment Model, which assigns high responsibility for cause, but low responsibility for solutions to the patinet. Ranking of the vignettees, t-tests, and scatter plots suggested that patients with psychosocial emergencies were assigned significantly more responsibiilty for cause and solution than patients with medical and surgical emergencies. Results of the ATP Survey revealed that nurses generally hold a positive attitude toward patients and prefer a nurturant and empathic nurse-patient relationship. Analysis revealed a significant positive relationship between the Moral Weakness attitude subscale and the Psychosocial Cause and Solution, and Overall Cause and Solution subscales. Based upon the findings of the study, implications for nursing practice, education, and research was addressed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".