Development of the C4 Inventory: A Measure of Common Characteristics that Complicate Care in Outpatient Psychiatry
Bibliographic record
Abstract
Background Psychiatric syndromes are complicated by comorbidity and other factors that burden patients, making guideline-informed psychiatric care challenging, and negatively affecting outcome. A comprehensive intake tool could improve the quality of care. Existing tools to quantify these characteristics do not identify specific complications and may not be sensitive to phenomena that are common in psychiatric outpatients. Objective To develop a practical inventory to capture observations related to complex care in psychiatric outpatients and quantify the overall burden of complicating factors. Design We developed a checklist inventory through literature review and clinical experience. The inventory was tested and compared with related measures in a cross-sectional study of 410 consenting outpatients at the time of initial assessment. Results The summed score of inventory checklist items was significantly correlated with patient-assessed measures of distress (K10, r=0.36) and function (WHODAS 2.0, r=0.31), and physician-assessed measures of function (GAF, r=−0.42), number of psychiatric diagnoses [ F(df3)=33.6], and most complex diagnosis [ F(df3)=37.4]. In 53 patients whose assessment was observed by two clinicians, inter-rater reliability was acceptable for both total inventory score (intraclass correlation, single measures = 0.74) and agreement on specific items (mean agreement score = 90%). Conclusions The Psychiatric C4 Inventory is a reliable instrument for psychiatrists that captures information that may be useful for quality improvement and resource planning. It demonstrates convergent validity with measures of patient distress, function, and complexity. Further tests of validity and replication in other settings are warranted.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".