The Compassion Balance: Understanding the Interrelation of Self- and Other-Compassion for Optimal Well-being

Sahdra, Baljinder K.; Ciarrochi, Joseph; Ferrari, Madeleine; Yap, Keong; Haller, Elisa; Hayes, Steven C.; Hofmann, S.; Gloster, Andrew T.

doi:10.31234/osf.io/8zw4d

Cited by 4 publications

(10 citation statements)

References 55 publications

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“…This can yield parameter estimates that are biassed if there are widely varying patterns of individual effects (Wright & Woods, 2020). In addition, multilevel modelimplied estimates for individuals in a sample have been shown to be driven towards the group level effect (Sahdra, Ciarrochi, Fraser, et al, 2022).…”

Section: Identifying Key Processes Of Changementioning

confidence: 99%

A personalised-approach to identifying important determinants of well-being

Ciarrochi¹,

Sahdra²,

Hayes³

et al. 2023

Preprint

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Background. Identifying the most important psychological drivers of well-being for a particular individual is critical to developing personalised interventions. Methods. We utilised three, intensive daily diary studies (within person measurement occasions N >50) across three data sets (n1=44; n2=37; n3=141) to examine within-person associations between clinically-relevant processes and a variety of outcomes. We utilised a novel idiographic algorithm, ”i-ARIMAX,” to calculate the strength of relationship (beta) between every process and every outcome within individuals. We then submitted all betas to meta-analytic methods. Results. All process-outcome links were highly heterogeneous between individuals. Processes that were associated with positive outcomes for some people were often unrelated to outcomes for others or associated with negative outcomes. Conclusion. i-ARIMAX might be used to guide personalised interventions and to reduce the number of candidate variables for complicated within-person analysis.

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Section: Identifying Key Processes Of Changementioning

confidence: 99%

A personalised-approach to identifying important determinants of well-being

Ciarrochi¹,

Sahdra²,

Hayes³

et al. 2023

Preprint

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“…All methods were approved by the local ethics committee (EKNZ-165/13) and all participants completed an informed consent. Data were derived from a transdiagnostic controlled effectiveness clinical trial focused on treating patients diagnosed with a DSM-IV disorder in an inpatient setting using Acceptance and Commitment Therapy (ACT), a common transdiagnostic intervention method (Ciarrochi et al, 2023;Sahdra et al, 2022).…”

Section: Participantsmentioning

confidence: 99%

When Average Isn't Enough: Identifying Meaningful Subgroups in Clinical Data

Gloster¹,

Nadler²,

Firsching-Block³

et al. 2023

Preprint

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Background: Clinical data are usually analyzed with the assumption that knowledge gathered from group averages applies to the individual. Doing so potentially obscures patients with meaningfully different trajectories of therapeutic change. Needed are “idionomic” methods that first examine idiographic patterns before nomothetic generalizations are made. The objective of this paper is to test whether such an idionomic method leads to different clinical conclusions.Methods: 51 patients completed weekly process measures and symptom severity over a period of eight weeks. Change trajectories were analyzed using a nomothetic approach and an idiographic approach with bottom-up clustering of similar individuals. The outcome was patients’ well-being at post-treatment.Results: Individuals differed in the extent that underlying processes were linked to symptoms. Average trend lines did not represent the intraindividual changes well. The idionomic approach readily identified subgroups of patients that differentially predicted distal outcomes (well-being).Conclusions: Relying exclusively on average results may lead to an oversight of intraindividual pathways. Characterizing data first using idiographic approaches led to more refined conclusions, which is clinically useful, scientifically rigorous, and may help advance individualized psychotherapy approaches.

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“…Idionomic methods-those that combine idiographic and nomothetic insights-have recently been touted to have great potential for informing individualized intervention, which is a core goal of process-based therapy Hayes et al, 2022;Sahdra et al, 2023).…”

Section: Testing the Applicability Of Idionomic Statistics In Longitu...mentioning

confidence: 99%

“…Assuming psychological homogeneity implies that a process would uniformly affect an outcome for everyone, treating individual differences as negligible 'error' (Michell, 2019;Richters, 2021). The ergodic assumptionthat group mean effects apply to the individual-underlies most of the nomothetic statistical tests used in psychology but is very often violated in models of human behavior Molenaar, 2008;Sahdra et al, 2023). Idiographic methods can accurately characterize individuallevel effects, but the challenge is to reconcile them with nomothetic effects (Lamiell, 1981), which are also important for scientific progress.…”

Section: The Psychological Homogeneity Assumptionmentioning

confidence: 99%

“…In contrast to a purely nomothetic approach or a purely idiographic approach, an idionomic orientation toward data analysis requires that analysts first examine individual-level (idiographic) data and then build and retain group-level (nomothetic) insights only if the group-level results help clarify the individual-level insights; hence, the term, 'idionomic' Hayes et al, 2022;Sahdra et al, 2023). An idionomic method does not refer to a particular statistical technique.…”

Section: The Psychological Homogeneity Assumptionmentioning

confidence: 99%

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Testing the Applicability of Idionomic Statistics in Longitudinal Studies: The Example of ‘Doing What Matters’

Sahdra,

Ciarrochi,

Klimczak

et al. 2023

Preprint

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According to the psychological flexibility model, “we hurt where we care, and we care where we hurt.” This suggests that values-based living and hedonic well-being are not always positively correlated, such as when engaging in valued action in the presence of important but stressful situations, which may reduce joy or increase sadness. Despite this prediction, past studies have focused on the average effects of valued living on hedonic well-being and found positive associations. In order to examine the heterogeneity of the within-person links of values-based action with hedonic well-being and the possibility of negative associations, we used intensive experience sampling methods (participants (n) = 425; 71.76% female; age = M(SD) = 22.20 (6.85); sampling design: 3-4 prompts per day; total measurements (n) = 6,456). Time series and meta-analytic models revealed that increases in values-based living were positively related to hedonic well-being, but the heterogeneity in this effect was so great (I2 > .80) that the average estimate did not apply to many people. Indeed, for some, valued action was associated with little joy and/or much sadness. A variety of aggregation methods based on idiographic models were examined. A sub-group was identified, which we labeled the ‘Stoics,’ for whom engagement in valued action in daily life was not contingent on sadness or joyfulness, unlike the Non-Stoics, as revealed in within-person networks. While overall levels of affect, valued action, and stressful events were the same in the two groups, network analysis further revealed that for Stoics, stressful situations were linked to valued action, but not hedonic well-being. For Non-Stoics, valued action was less likely in stressful situations, but when valued action did occur it was associated with more joy and less sadness. We discuss the implications of these results for personalizing interventions.

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The Compassion Balance: Understanding the Interrelation of Self- and Other-Compassion for Optimal Well-being

Cited by 4 publications

References 55 publications

A personalised-approach to identifying important determinants of well-being

A personalised-approach to identifying important determinants of well-being

When Average Isn't Enough: Identifying Meaningful Subgroups in Clinical Data

Testing the Applicability of Idionomic Statistics in Longitudinal Studies: The Example of ‘Doing What Matters’

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