The aim of the study was to investigate how nurses detected and reported physiological deterioration in acutely ill patients. To date there is a paucity of research on how nurses pick up deterioration. The emphasis within the literature tends to be on identifying premonitory signs that may be useful in predicting patients who are in danger of deterioration. Changes in respiratory rate is the most consistent in picking up such patients (Fieselmann et al. 1993; Sax and Charlson 1987; Schein et al. 1990; Smith and Wood 1998) but in common with other signs, it lacks sensitivity and specificity. The sample consisted of 44 nurses, doctors and health care support workers working on a general medical and surgical ward. Data were collected by means of non-participant observations and interviews, using grounded theory as originated by (Glaser and Strauss 1967) and (Glaser 1978). As data were collected, the consistent comparative method and theoretical sensitivity were used as outlined in grounded theory. A theory of "making credible" emerged, together with its sub-core categories of "intuitive knowing", "contextualising" and "grabbing attention". The problem that nurses face in referring patients they suspect are deteriorating is in persuading doctors to come and review them. How nurses deal with this is a three stage process. Through intuitive knowing they pick up that patients have changed in a way that requires a medical assessment. To make the referral more credible, nurses attempt to contextualise changes by establishing baselines of how patients are in terms of their progression and vital signs and continual vigilance. Finally with the backup of colleagues, nurses refer patients by providing as much persuasive information as possible. The whole process is facilitated by knowledge and experience, together with mutual trust and respect. Cautiousness characterises each step.
Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:502597 |
Date | January 2004 |
Creators | Andrews, Thomas |
Publisher | University of Manchester |
Source Sets | Ethos UK |
Detected Language | English |
Type | Electronic Thesis or Dissertation |
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