Initial efforts to enhance the R2MR Mental Health Continuum Model
Bibliographic record
Abstract
Introduction: The Road to Mental Readiness (R2MR) Mental Health Continuum Model (MHCM) is a mental health self-monitoring tool that allows individuals to self-map their mental health status on six domains using descriptors ranging in severity from healthy to reacting, injured, ill. The goals were to improve the validity of this tool by adding sub-domain descriptors from each domain (Study 1) and assessing whether the addition of these sub-domain descriptors to the domain descriptors resulted in more accurate self-mapping along the MHCM (Study 2). Methods: In Study 1, 159 Regular Force members completed a matching task in which they were presented with descriptors and asked to select the MHCM anchor that each belonged to. In Study 2, 274 Regular Force members completed several validated scales and mapped themselves on the MHCM using the descriptors selected in Study 1. Results: In Study 1, subsets of sub-domain descriptors were selected that optimized the accuracy mapping scores for all domains and most anchor categories. In Study 2, the addition of sub-domain descriptors enhanced the validity of the MHCM tool for all domains and was associated with the highest correlations with validated scales; most coefficients exceeded concurrent validity thresholds. Discussion: These findings provide initial support for the enhanced validity of a MHCM tool that should allow military members to better identify changes in their mental health status.
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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 | 0.062 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".