Interlink decision making index (IDMI) -- a simple tool/concept to assist decision making for sustainability Part 1: definition of IDMI

There is an apparent gap between the LCA or other assessment's outcomes and its effective application in the decision making process. It is needed to provide to the decision makers a simple, less human interfered mechanism that integrates all the key criteria (environmental, economic, technical and safety etc.). The proposed index: Interlink Decision Making Index (IDMI) has all these features: simple, interlink (all criteria) and automatically and quantified influence of critical criteria (ie. no human weighting needed) and is able to assist the multi-criteria decision making for sustainability based on the outcomes of specific assessments (eg. LCA, ElA etc.). The index represents a pure numerical value and does not necessarily have any physical meanings, but it reflects the total merits of a particular option once the normal decision making criteria and (up to two) critical criteria (CC) have been chosen. Then, without arbitrarily weighting process, the comparison and selection of the best possible option, ie. decision can be made based on the derived IDMI results. Two hypothetical examples are presented in part 2 of the paper to demonstrate the application of the IDMI concept and it's differences with the traditional "tabular method" in the decision making process.

Interlink decision making index (IDMI) : a simple tool/concept to assist decision making for sustainability part 2 : calculation and examples of IDMI

There is an apparent gap between the LCA or other assessment's outcomes and its effective application in the decision making process. It is needed to provide to the decision makers a simple, less human interfered mechanism that integrates all the key criteria (environmental, economic, technical and safety etc.). The proposed index: Interlink Decision Making Index (IDMI) has all these features: simple, interlink (all criteria) and automatically and quantified influence of critical criteria (ie. no human weighting needed) and is able to assist the multi-criteria decision making for sustainability based on the outcomes of specific assessments (eg. LCA, BIA etc.). The index represents a pure numerical value and does not necessarily have any physical meanings, but it reflects the total merits of a particular option once the normal decision making criteria and (up to two) critical criteria (Ce) have been chosen. Then, without arbitrarily weighting process, the comparison and selection of the best possible option, ie. decision can be made based on the derived IDMI results. Two hypothetical examples are presented in the part 2 of the paper to demonstrate the application of the IDMI concept and it's differences with the traditional "tabular method" in the decision making process.

Critical care nurses' decision-making activities in the natural clinical setting

• This article reports on observation of 18 nurses in urban and rural based critical care settings.

• The purpose of the study was to observe and describe the decision-making activities of critical care nurses within natural clinical settings.

• During the 2-hour observation, the researcher dictated a detailed commentary on to audio-tape of each nurse's actions. Tapes were transcribed and subjected to content analysis.

• Findings indicated three main categories of decisions. Decision frequencies were linked to nurses' critical care experience, appointment level, and location, as well as nursing shifts.

• The findings are discussed in relation to previous empirical evidence and the implications for practice.

• The author concludes that future research should be directed towards measuring the contextual influences on nurses' decision-making on the outcome of patient care.

Informing decision-making in libraries: informetric research as input to LIS education and practice.(Library and Information Science).

Findings from informetric research represent an important background resource to add to the mix of information useful for resolving difficult and ongoing problems in specific library environments or information service settings. This paper provides examples of informetric research that can be useful input to decision-making in the field of library management and information service provision. This overview takes four of the challenges that Michael Buckland outlined for library research as a way of guiding the discussion of ways that informetric work can be used to inform library decision-making. (1) References are made to relevant informetric work undertaken or conducted in Australia, by Australian researchers, or with Australian data.

Informetrics includes both quantitative and qualitative methods, which when used in combination can provide a rounded set of findings that has great validity for management, policy and service applications. Quantitative methodologies are generally based on bibliometric techniques, such as mining and analysis of data from various bibliographic and textual databases. Qualitative methods include survey, case study and historical approaches. Used in combination, each set of findings adds richness and other perspectives to an analysis.

Use of a decision aid for prenatal testing of fetal abnormalities to improve women's informed decision making: a cluster randomised controlled trial [ISRCTN22532458]

Objective To evaluate the effectiveness of a decision aid for prenatal testing of fetal abnormalities compared with a pamphlet in supporting women's decision making.
Design A cluster randomised controlled trial.
Setting Primary health care.
Population Women in early pregnancy consulting a GP.
Methods GPs were randomised to provide women with either a decision aid or a pamphlet. The decision aid was a 24-page booklet designed using the Ottowa Decision Framework. The pamphlet was an existing resource available in the trial setting.
Main outcome measures Validated scales were used to measure the primary outcomes, informed choice and decisional conflict, and the secondary outcomes, anxiety, depression, attitudes to the pregnancy/fetus and acceptability of the resource. Outcomes were measured at 14 weeks of gestation from questionnaires that women completed and returned by post.
Findings Women in the intervention group were more likely to make an informed decision 76% (126/165) than those in the control group 65% (107/165) (adjusted OR 2.08; 95% CI 1.14–3.81). A greater proportion of women in the intervention group 88% (147/167) had a 'good' level of knowledge than those in the control group 72% (123/171) (adjusted OR 3.43; 95% CI 1.79–6.58). Mean (SD) decisional conflict scores were low in both groups, decision aid 1.71 (0.49), pamphlet 1.65 (0.55) (adjusted mean difference 0.10; 95% CI −0.02 to 0.22). There was no strong evidence of differences between the trial arms in the measures of psychological or acceptability outcomes.
Conclusion A tailored prenatal testing decision aid plays an important role in improving women's knowledge of first and second trimester screening tests and assisting them to make decisions about screening and diagnostic tests that are consistent with their values.

Utility of real-time decision-making in commercial data stream mining domains

The objective is to measure utility of real-time commercial decision making. It is important due to a higher possibility of mistakes in real-time decisions, problems with recording actual occurrences, and significant costs associated with predictions produced by algorithms. The first contribution is to use overall utility and represent individual utility with a monetary value instead of a prediction. The second is to calculate the benefit from predictions using the utility-based decision threshold. The third is to incorporate cost of predictions. For experiments, overall utility is used to evaluate communal and spike detection, and their adaptive versions. The overall utility results show that with fewer alerts, communal detection is better than spike detection. With more alerts, adaptive communal and spike detection are better than their static versions. To maximise overall utility with all algorithms, only 1% to 4% in the highest predictions should be alerts.