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Record W4402931722 · doi:10.1080/23279095.2024.2407462

Inventory of sensory, emotional, and cognitive reserve (SECri): Proposal of a new instrument and preliminary data

2024· article· en· W4402931722 on OpenAlexaboutno aff
Joana O. Pinto, Isabel Vieira, Beatriz Barroso, Miguel Peixoto, Diogo Pontes, Bruno Peixoto, Artemisa R. Dores, Fernando Barbosa

Bibliographic record

VenueApplied Neuropsychology Adult · 2024
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCognitionSensory systemCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.344
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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