A Good Enough Measure? Assessing Existential Maturation

Existential maturation describes a developmental process that responds to the realities of mortality. Studies of existential maturation have been limited by the lack of a measure for it. Building on a clinically based conceptual model and comments by patients that clinicians might use to discern a patient’s state of mind, we developed what appears to be a good enough measure. To clarify how the 10-factor conceptual model can be represented in an overall point-in-time measure, we categorized the factors into input, impact modifying, response modifying, and clinical outcomes. We then recalled and paraphrased comments that caused the clinician to be alert to aspects of existential maturation. These 77 items comprised our first pool of potential items. Using a Qualtrics survey, we conducted a Delphi study with a panel of psychoanalysts and palliative care clinicians with expertise in the area. Of the 21 recruited clinicians, 17 (81%) participated. We ranked items according to the proportion of respondents who found each item relevant and important. The 26 items that scored well were retained. Review suggested a need to better capture the oscillations a person goes through. An additional item was added, making it a 27-item survey. All items were given introductory words suitable for a general population questionnaire and a 0–100 visual analogue scale. Using a Qualtrics panel recruitment approach, a general population of 510 adults completed the survey. The items had means from 31.9 ± 31.7 to 65.8 ± 28.1. The large standard deviations indicate that the respondents varied in how much they could imagine themselves making such a comment. The respondents’ answers showed an acceptable level of consistency across the 27 items (Guttman’s Lambda-6 = 0.84). Respondents’ open-ended comments suggested that the questionnaire was generally experienced as acceptable and thought-provoking, with many describing it as an unusual but meaningful opportunity to reflect on life and death. The measure does not capture the nonlinear iterative nature of the model directly. To assess changes, multiple times would be assessed. Future work will need to evaluate the measure for construct and discriminant validity and test–retest reliability.