On Suffering: Pathways to Healing and Health

10. Standards of Care

10 * Standards of Care Normative data are obtained from tests and scales and are used for comparison of results from one group or individual to the means and standard deviations of another group or to individual sets of measures. They delineate that which usually occurs in a specific population at a specific point in time and are an important treatment guide, particularly when specific treatment interventions such as drug therapy are being considered. Normative data are important in developing standards of care. The ability to “place” patients along a continuum of response or clinical outcome, for example, is useful when clinicians must make determinations of when to refer patients to other health care professionals or to track significance of specific interventions. Normative data information about suffering is essential after patients are involved in accidents or when it is important to determine the significance of chronicity, in situations involving litigation or disability compensation, or when crisis intervention is required (1–9). The purpose of this chapter is to: (a) demonstrate the need for comparative data, (b) report the normative data results of suffering and pain scores obtained from a sample of 166 persons with arthritis and 100 individuals with epilepsy, (c) provide instructions about how to calculate normative scores from raw data, and (d) pre­ sent examples of clinical interpretation of normative data. How Does the Patient Compare with Persons Who Have Similar Disorders? Normative data scoring methods are used when a clinician wishes to compare an individual patient’s scores with those of a larger sample or a similar comparison group. To date, only complete data sets for the construct of suffering for persons with epilepsy and those with arthritis are available (10). These illnesses are representative of the extremes of the suffering and pain experiences (epilepsy— less suffering, and arthritis—severe suffering): therefore, it is possible to “place” other persons along this continuum. Normative data scoring is important because its interpretation is thought to be more useful clinically than raw data scores, and allows health care professionals to better ascertain the dimensions of an individual ’s clinical complaints. To date, there is no gold standard for suffering per se, but preliminary descriptive inferential statistics (mean, standard deviation, and resultant Z scores) have been derived from the 166 patients with arthritis and 100 persons with epilepsy introduced earlier. Z scores are useful because they indicate Standards of Care 149 how many standard deviations an element is from the mean. If a patient’s score is below the mean of that of the reference sample, i.e., a negative Z score, the results are rated as favourable (little or no distress). A Z score of zero indicates moderate distress and a Z score above the mean, a positive Z score, indicates that the patient is in severe distress and requires immediate attention. How Is the Z Score Calculated? The Z score is simply computed as follows: Z = (x − x̄) / SD Where: x = the individual patient’s suffering score from one of the masq subscales x̄ = the mean suffering subscale score based on an established sample SD = the standard deviation based on an established sample The following values, obtained from two sample groups that are representative of the problems of suffering and pain can be used for comparison purposes. The arthritis sample represents comparative values for individuals with chronic illness who usually experience considerable suffering. The epilepsy sample is representative of persons who have chronic illness but usually experience little suffering. The information below is needed to convert raw data results into normative scores. Reference Scores table 10.2 · Arthritis (N = 166) Suffering Subscale Scores Suffering subscale Mean / x̄ ± SD Idea of self 4.01 ± 0.742 Relationships 3.29 ± 0.952 Response to illness 3.01 ± 0.808 Coping with life 2.78 ± 0.724 table 10.1 · Arthritic Sample Results Arthritis (N = 166) Mean/x̄ ± SD Total suffering score 3.27 ± 0.655 Total pain score 2.89 ± 0.597 Total work beliefs score N/A Total self-efficacy score N/A Note: N = total number of individuals in the sample. 150 Identifying Those Who Suffer Instructions for Converting Raw Results into Normative Data Scores There are six basic steps to follow to determine how patients compare with similar groups: Step 1: Administer the nineteen-item suffering component of the masq. Each item is scored on a scale of 1 to 5, where a low score is best...