State-Space Grid Analysis: Applications for Clinical Whole Systems Complementary and Alternative Medicine Research
Bibliographic record
Abstract
This paper presents state space grids (SSGs) as a mathematically less intensive methodology for process-oriented research beyond traditional qualitative and quantitative approaches in whole systems of complementary and alternative medicine (WS-CAM). SSGs, originally applied in developmental psychology research, offer a logical, flexible, and accessible tool for capturing emergent changes in the temporal dynamics of patient behaviors, manifestations of resilience, and outcomes. The SSG method generates a two-dimensional visualization and quantification of the inter-relationships between variables on a moment-to-moment basis. SSGs can describe dyadic interactive behavior in real time and, followed longitudinally, allow evaluation of how change occurs over extended time periods. Practice theories of WS-CAM encompass the holistic health concept of whole-person outcomes, including nonlinear pathways to complex, multidimensional changes. Understanding how the patient as a living system arrives at these outcomes requires studying the process of healing, e.g., sudden abrupt worsening and/or improvements, 'healing crises', and 'unstuckness', from which the multiple inter-personal and intra-personal outcomes emerge. SSGs can document the indirect, emergent dynamic effects of interventions, transitional phases, and the mutual interaction of patient and environment that underlie the healing process. Two WS-CAM research exemplars are provided to demonstrate the feasibility of using SSGs in both dyadic and within-patient contexts, and to illustrate the possibilities for clinically relevant, process-focused hypotheses. This type of research has the potential to help clinicians select, modify and optimize treatment plans earlier in the course of care and produce more successful outcomes for more patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".