INCRESE: Development of an Inventory to Characterize Recorded Mental Health Recovery Narratives.
Bibliographic record
Abstract
OBJECTIVE: Mental health recovery narratives are increasingly used in clinical practice, public health campaigns, and as directly-accessed online resources. No instrument exists to describe characteristics of individual recovery narratives. The aims were to develop and evaluate an inventory to characterize recorded recovery narratives. RESEARCH DESIGN AND METHODS: A preliminary version of the Inventory of Characteristics of Recovery Stories (INCRESE) was generated from an existing theory-base. Feasibility and acceptability were evaluated by two coders each rating 30 purposively-selected narratives. A refined version was produced and a formal evaluation conducted. Reliability was assessed by four coders each rating 95 purposively-selected narratives. Inter-coder reliability was assessed using Fleiss's kappa coefficients; test-retest reliability was assessed using intra-class correlation coefficients (ICCs). RESULTS: Multiple refinements to description, coding categories, and language were made. Data completeness was high, and no floor or ceiling effects were found. Intercoder reliability ranged from moderate (k=0.58) to perfect (k=1.00) agreement. Test-retest reliability ranged from moderate (ICC=0.57) to complete (ICC=1.00) agreement. The final INCRESE comprises 77 items spanning five sections: Narrative Eligibility; Narrative Mode; Narrator Characteristics; Narrative Characteristics; Narrative Content. CONCLUSION: INCRESE is the first evaluated tool to characterize mental health recovery narratives. It addresses current concerns around normative recovery narratives being used to promote compulsory wellness, e.g. by identifying narratives that reject diagnosis as an explanatory model and those with non-upward trajectories. INCRESE can be used to establish the diversity of a narrative collection and will be used in the NEON trials (ISRCTN11152837, ISRCTN63197153, ISRCTN76355273) to allow a recommender system to match narratives to participants.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".