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Record W7064172786

Attitudes and attributions of Winnipeg emergency nurses : a correlational self-report survey

2012· dissertation· en· W7064172786 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2012
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsAttributionDispositionCognitionMeaning (existential)Coping (psychology)Health care
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.260
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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