Inventory of sensory, emotional, and cognitive reserve (SECri): Proposal of a new instrument and preliminary data
Bibliographic record
Abstract
A new model of reserve, the Sensory, Emotional, and Cognitive Reserve (SEC) model, has been recently proposed, but so far this model has not been operationalized in instruments to evaluate the different domains of the reserve. This study introduces the SEC reserve inventory (SECri) along with preliminary data obtained from a study involving 57 adults, aged 35 and older. The SECri assesses the SEC domains using specific proxies: (a) sensory reserve (SR) through sensory acuity and sensory perception proxies; (b) emotional reserve (ER) through life events, resilience, and emotional regulation proxies; and (c) cognitive reserve (CR) through education, occupation, socioeconomic status, bilingualism, leisure activities, and personality traits proxies. Key features of SECri include self- and informant-report forms, fine-grained response scales, and the evaluation of reserve development across the lifespan. Findings on the acceptability, convergent validity between SECri domains and validated tests for the same constructs, internal consistency of each domain, and predictive validity of Montreal Cognitive Assessment scores support further research with this inventory. Future studies should consider determining SECri's psychometric properties in clinical and subclinical conditions to evaluate its prognostic value in cases of neurocognitive decline.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".