assess-relationship-health

Computing affect ratios and repair rates requires SQL templates applied to structured affect datasets over 28-day and weekly rolling windows. The analysis categorizes relationship health into Healthy, At-Risk, or Concerning states and provides diagnostic guidance for clinical intervention planning.

The Gottman SPAFF affect ratio quantifies relationship health by measuring positive to negative affect interactions over defined periods. Tracking this ratio over 28-day and weekly rolling windows reveals behavioral patterns and inflection points, categorizing relationship stability as Healthy, At-Risk, or Concerning for clinical coaching settings.

Yes, SQL analysis identifies EFT patterns and inflection points by applying SQL templates to structured affect datasets. This approach quantifies repair rates and detects causal conversations, enabling clinicians to track couple relationship trends and flag transitions from stable to at-risk for targeted interventions.

Relationship health assessment supports both 28-day and weekly rolling granularity for tracking couple trends in clinical or coaching settings. This dual-window approach monitors short-term pattern shifts and long-term affect ratio stability, generating structured outputs for health categorization and targeted intervention planning.

Structured outputs categorize relationship health into Healthy, At-Risk, and Concerning states. These outputs include quantified affect ratios, repair rates, and diagnostic guidance derived from pattern detection and causal conversations, directly informing clinical decisions and targeted intervention strategies.