PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 6551426

Strategic planning for nursing.

D H Fox·R T Fox
View original source ↗Export metadata ↓

Abstract

Strategic planning is a systematic approach to decision making. Nurse administrators who rely on formal planning and measurement of outcomes are more likely to assure consistent high performance from their departments than those who rely on intuition. Planning can serve as motivation for nursing personnel if they are allowed to have some input into it. Not only is nursing practice advanced by strategic planning, as this article shows, but scarce resources can be conserved.

Explore related subjects

Data CollectionDecision MakingEvaluation Studies as TopicGoalsGroup ProcessesNursing ServicesPolicy MakingJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D H Fox, R T Fox. 1983. Strategic planning for nursing.. https://pubmed.ncbi.nlm.nih.gov/6551426/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Probability estimation when some observations are grouped.

This paper considers the use of additional questions for decreasing survey non-response rates and an approach for estimating a probability based on the results obtained. In a survey, the respondents are asked to answer an original question and follow-up questions, where the answers for the follow-up questions are grouped answers for the original question. For example, respondents are asked to provide an exact number of incidents, but in cases of 'Do not know' or 'Refuse' responses, they are subsequently asked to pick an answer from a less specific categorical scale. The new estimator obtains smaller variance asymptotically and does not depend on a distribution family. This method is applied to income questions in a survey regarding injury prevention and behaviours. Another application is survey data on intimate partner violence, where some amendments were applied for incorporating post-stratification weights and for using non-random grouping. For additional illustration, an example of parameter estimation on artificially generated data is presented.

Data Collection↗