Outcomes Intermediate 2nd Edition Pdf

[Cary Polachek]

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Importance: As many as 10% of women experience natural menopause by the age of 45 years. If confirmed, an increased risk of cardiovascular disease (CVD) and all-cause mortality associated with premature and early-onset menopause could be an important factor affecting risk of disease and mortality among middle-aged and older women.

Objective: To systematically review and meta-analyze studies evaluating the effect of age at onset of menopause and duration since onset of menopause on intermediate CVD end points, CVD outcomes, and all-cause mortality.

Study selection: Studies (ie, observational cohort, case-control, or cross-sectional) that assessed age at onset of menopause and/or time since onset of menopause as exposures as well as risk of cardiovascular outcomes and intermediate CVD end points in perimenopausal, menopausal, or postmenopausal women.

Data extraction and synthesis: Studies were sought if they were observational cohort, case-control, or cross-sectional studies; reported on age at onset of menopause and/or time since onset of menopause as exposures; and assessed associations with risk of CVD-related outcomes, all-cause mortality, or intermediate CVD end points. Data were extracted by 2 independent reviewers using a predesigned data collection form. The inverse-variance weighted method was used to combine relative risks to produce a pooled relative risk using random-effects models to allow for between-study heterogeneity.

Main outcomes and measures: Cardiovascular disease outcomes (ie, composite CVD, fatal and nonfatal coronary heart disease [CHD], and overall stroke and stroke mortality), CVD mortality, all-cause mortality, and intermediate CVD end points.

One difference could be the length of time between the program intervention and the measured outcome, however the most important difference is the effect the intervention has on the outcome. Short-term outcomes can be directly tied to the intervention, while long-term outcomes can be less directly attributed to the program. In general, short-term outcomes are measured at the end of the program or soon after the program has finished. Short-term outcomes refer to changes in knowledge, attitudes, or behaviors and can include reports of behaviors that participants intend to change or motivation to change. Intermediate outcomes are usually measured within several months after the end of the program and include actions by participants based on what they learned. Long-term outcomes are measured a year or several years after program completion and include changes in conditions, policies, or organizational structure. For example, a short-term outcome for a smoking prevention program for teenagers could be the number of teens who report that they do not plan to start smoking. An intermediate outcome could be the number of teens who report not smoking at six months, and a long-term outcome could be a reduction in the smoking rate among teens in a city, county, state or region. Short-term, intermediate, and long-term outcomes are related and build on each other.

This work is supported in part by New Technologies for Agriculture Extension grant no. 2020-41595-30123 from the USDA National Institute of Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.

It is quite clear that one should not control for post-treatment variables / intermediate outcomes when the goal is causal inference, but I wasn't sure if the same advice should hold when one's goal is to build a model for prediction.

Here is some context for my question: I'm trying to build a model that predicts if a college student will earn a bachelor's degree within 6 years of high school graduation using a large observational data set. I have data on students' high school variables (HS GPA, test scores, number activities participated in, etc.), some data on the students' college experiences (delayed enrollment in college, full-time / part-time status, transferred within two years of enrolling), as well as data on the characteristics of the college they attend. In other words, I have student level and institutional level data. I would account for the nesting of students within a particular institution.

Some have told me that the student level data on college experiences are intermediate outcomes and I shouldn't include them in the model. It isn't clear to me if I should / could include the college experience variables (which could be considered intermediate outcomes) in the predictive model, and if I do include them, how they should be treated.

I'd strongly discourage including those intermediate outcomes, for the sake of interpreting the parameters. Let's translate "predicted difference in the response of two individuals that differ by one unit on the regressor in question and that have the same value on all other regressors" into the terms of the study question: if you include the intermediate outcomes in the model, you're only comparing students who delayed enrollment to others who delayed enrollment, and comparing students who went to similar colleges to each other. If students with lower high school GPA are also more likely to delay college enrollment, and students who go to private colleges have higher GPAs, a model that includes both variables will only be useful for predicting a student finishing college based on their GPA if you also already know what kind of college they went to. Is that the prediction you need?

I have worked at The Brilliant Club for the last six years as their Research and Impact Director, and when I joined the world of Widening Participation (WP) it certainly was a buzz of activity. There were so many things going on and the pace at which the sector was able to create, adapt and scale programmes to support disadvantaged students was truly remarkable.

The same is still very much true today, and even more significant given the effects of the pandemic on student learning. The key difference now, I think, is that programme evaluation is no longer an optional nice-to-have, instead it is very much needed, wanted and expected by everyone working in WP.

There is an emerging consensus that Higher Education Providers (HEPs) and sector organisations need to focus on intermediate outcomes as well as long-term outcomes to get a more holistic understanding of the impact of WP programmes. By intermediate outcomes, we mean outcomes that happen following an intervention that contribute to long-term outcomes. Intermediate outcomes can include changes in behaviour, skills and attitudes whereas long-term outcomes tend to be measured using behavioural outcomes.

In the case of WP, we often identify progression to university as the long-term outcome, although more and more this is being broadened to include student success outcomes as well (e.g. progression through a degree; degree classification). What we know is that a whole host of intermediate outcomes are helping to drive these long-term outcomes.

The Brilliant Club, like many organisations, has been on the same outcomes journey and after using our own survey items and then using items from the research literature, we are now confidently adopting the hybrid approach.

These findings have helped to inform our thinking at The Brilliant Club about the relevance of higher-order skills within WP programmes. More widely, as a charity, we will continue to promote the interplay between WP and student success work so that we can better understand the outcomes that really matter to students.

At this point, your knowledge exchange initiative should be anchored in the development goal and a change objective. While it is possible to reach some change objectives just using knowledge exchange, it is not very common. Since knowledge exchange is almost always a part of a larger development effort, it is more likely to catalyze progress towards the change objective than to achieve the objective on its own. This progress is measured by the achievement of intermediate outcomes.

Intermediate outcomes are what we most commonly expect to see, measure, and report after a knowledge exchange initiative. They reflect what participants want to learn, how and with whom they want to work, and how they want to act.

Think of intermediate outcomes as stepping stones leading to the change objective. Knowledge exchange can move your participants toward the objective by helping them address cognitive (know why), relational (know who), and behavioral (know how) gaps. Work with your counterparts to determine what gaps to tackle first and how knowledge exchange can address them.

When defining the intermediate outcomes, think first about what personal or group dynamics are preventing progress towards the change objective. Perhaps participants are not sure about how to address a challenge. Or it maybe they disagree on the way forward. Another possibility is that your counterparts seek ways to take an already successful situation to the next level.

Along with defining the intermediate outcomes, you will need to figure out how to measure their achievement. That is, you will need to identify indicators that show participants have learned or changed in the desired way. Table 2 will help you think through possible intermediate outcomes and indicators.

New Knowledge: Baseline and follow-up surveys with Honduran stakeholders will reveal improved knowledge of legal frameworks, stakeholder roles, consultation procedures, and governance of communal lands.

Enhanced Skills: Representatives of key public agencies responsible for implementing land titling and land regulation will develop proficiency in the process of demarcation and titling of indigenous territories.

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