Smoking has been evidently linked with the progression and exacerbation of coronary and hence cardiac conditions. Plaguing of the coronary arterial lumen due to smoking leads to the narrowing of the arterial lumen, hence leading to blockage of the coronary artery and subsequently, reduced cardiac output and the progression of a cardiac condition (Lloyd-Jones et al., 2010). In this regard, smoking cessation is pretty essential in arresting the exacerbation or progression of coronary disease after a post coronary intervention.
In any health promotion intervention, evaluation of outcomes becomes an integral component as it places the implementer at a vantage point in terms of understanding the overall score or effectiveness of the intervention (McKenzie et al., 2016). In this regard, it is pretty essential to identify the various variables or data sets to use in evaluation and subsequently, coming up with a strategy for data collection. Contextually, this counseling on smoking cessation for post coronary intervention seeks to achieve various objectives including the inculcation of self-efficacy, promoting smoking cessation and ultimately, bringing various clinical variables within control and optimal management of the patient.
For this purpose, the variables or the metrics that will be used to evaluate this project include both clinical and non-clinical metrics. The clinical metrics include; recovery after a coronary intervention, hospital re-admissions due to exacerbation, mortality, morbidity and prevalence of various risk factors for cardiac conditions associated with smoking such as diabetes and hypertension (McKenzie et al., 2016). On the other hand, the non-clinical variables include; patient satisfaction with the program, self-reported improvements, smoking cessation and the general perception of individual patients with regard to quality of life and health status. This being the case, data related to these metrics would be collected both objective (quantitatively) and subjectively (qualitatively). For clinical variables, a quantitative/objective approach would be used since these variables are essentially objective in nature. As such, a retrospective look of the patient’s clinical records provides an effective way of obtaining this data. On the other hand, for non-clinical variables, it is quite clear that the metrics are subjective in nature and this data would fundamentally rely on an individual patient’s subjective opinion and perceptions. An open-ended questionnaire would thus be used to collect this type of data.
Cost is an equally important metric for evaluating the success of the smoking cessation program. For the counselor, the overall cost of the undertaking would be used to rate the cost effectiveness of the program. Cost effectiveness of any program is a goal that an implementer pursues to achieve since cost in a major health outcome determinant. Apart from the implementer, the cost effectiveness of the project to the patient’s side is also a concern as far as evaluation is concerned. The beauty of this evaluation is that it will go a long in the formative and summative assessment of the counseling program on smoking cessation. The formative component helps in assessing the effectiveness of a program along the course of its implementation, whereby the formative findings helps the implementer in understanding the adjustments or improvements that may be made along the course of a program in a bid to influence the achievement of the desired final project outcomes (McKenzie et al., 2016). On the other hand, the summative findings helps in mapping the overall success or score-card for the entire program in the very end in terms of meeting the intended objectives (McKenzie et al., 2016).
The table below is an outline of the various variables or metrics that would be used to evaluate the counseling program on smoking cessation among patients after a coronary intervention. The successful achievement of the desired outcomes for each of the below listed variable would signal a high level of success for the program.
Data Structure Table
References
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