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Biomedical subjects

D N Glaser

Publications and source records attributed to D N Glaser.

5 recordsLinked to original sources
PubMed1999

Pain assessment and management in critically ill postoperative and trauma patients: a multisite study.

BACKGROUND: Pain in critically ill patients is undertreated. OBJECTIVES: To examine patients' perceptions of pain and acute pain management practices in a large metropolitan area to provide direction for improvements in pain relief. METHODS: In a descriptive, correlational study, data were collected from 213 patients in 13 hospitals. Interviews with patients, chart reviews, and interviews with nurse leaders were used to examine institutional and individual approaches to pain management. RESULTS: Twenty-eight percent of patients did not recall an explanation of a pain management plan, and 64% were often in moderate to severe pain while in the intensive care unit. High pain intensity correlated with wait for an analgesic (P < .001), expectations of less pain (P < .001), and longer stay in the intensive care unit (P < .001). Low satisfaction correlated with expectations of less pain (P < .001), often being in moderate to severe pain (P < .001), and long wait for an analgesic (P < .001). In the first 24 hours postoperatively, only 54% of patients had a numerical pain rating documented; 91% had a pain description. The amount of opioid given on postoperative day 1 was influenced by pain intensity (P < .001), the patient's age (P = .03), type of surgery (P = .002), and route of analgesic (P < .001). Only 33% of patients had nonpharmacological pain interventions documented. CONCLUSIONS: Despite moderate to severe pain, patients are generally satisfied with their pain relief. Measuring patients' satisfaction alone is not a reliable outcome for determining the effectiveness of pain management. Realistic expectations of patients about their pain may enhance coping, increase satisfaction, and decrease pain intensity after surgery.

Aged↗
PubMed1999

The controversy of significance testing: misconceptions and alternatives.

The current debate about the merits of null hypothesis significance testing, even though provocative, is not particularly novel. The significance testing approach has had defenders and opponents for decades, especially within the social sciences, where reliance on the use of significance testing has historically been heavy. The primary concerns have been (1) the misuse of significance testing, (2) the misinterpretation of P values, and (3) the lack of accompanying statistics, such as effect sizes and confidence intervals, that would provide a broader picture into the researcher's data analysis and interpretation. This article presents the current thinking, both in favor and against, on significance testing, the virtually unanimous support for reporting effect sizes alongside P values, and the overall implications for practice and application.

Bias↗
PubMed1996

Tutorial: causal modeling and patient satisfaction.

Causal modeling is used in a variety of sciences because it allows exploration of complex relationships among several variables simultaneously. Although not used extensively in health care as yet, causal modeling could be helpful, given the complexity of the current health care system. The purpose of this article is to provide a general introduction to causal modeling and the syntax used in developing and testing a model. To illustrate the method, a sample model is tested using a combination of hypothetical and actual patient satisfaction data.

Data Interpretation, Statistical↗