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Record W7009605293

Exploring the Interactions Between Cognitive Impairment, Depression, and Growth Mindset Among African Americans in the COVID-19 Era

2023· article· en· W7009605293 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalCommons - Kennesaw State University (Kennesaw State University) · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLiterary Theory and Cultural Hermeneutics
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetCognitionDysfunctional familyFeelingPsychosocialDepression (economics)Affect (linguistics)Major depressive disorder
DOInot available

Abstract

fetched live from OpenAlex

Depression is a common and complex psychiatric disorder that can affect people of all ages, genders, and backgrounds. It is currently characterized by persistent feelings of sadness, hopelessness, and lack of interest in life (Depressive Disorders, n.d.). Depression can have an impact on an individual's attitude in life such as their growth mindset. A Growth mindset is an individual’s belief that their abilities can be developed and improved through effort and learning (Dweck, 2016). This way of thinking can lead to increased resilience, learning, and achievement. Another area that can impact depression and a growth mindset is cognitive dysfunction. Cognitive dysfunction refers to a range of difficulties in cognitive functioning, such as problems with attention, memory, and decision-making (Lam et al., 2014). This current study seeks to examine the influence of cognitive dysfunctions on the relationship between growth mindset and depression throughout the height of the COVID-19 pandemic. A total of N = 312 African American men answered a survey using Qualtrics. Results suggest that cognitive dysfunction fully mediated the relationship between growth mindsets and depression. Findings suggest that while developing a growth mindset is important for reinforcing a resilient perspective, reducing dysfunctional cognitions may be a necessary component of growth. Keywords: African American, cognitive dysfunction, COVID-19, depression, growth mindset Sources: Lam, R. W., Kennedy, S. H., Mclntyre, R. S., & Khullar, A. (2014). Cognitive dysfunction in major depressive disorder: effects on psychosocial functioning and implications for treatment. Canadian journal of psychiatry. Revue canadienne de psychiatrie, 59(12), 649–654. https://doi.org/10.1177/070674371405901206 Depressive Disorders. Psychiatry Online. (n.d.). Retrieved March 14, 2023, from https://dsm.psychiatryonline.org/doi/full/10.1176/appi.books.9780890425787.x04_Depressive_Disorders Dweck, C. (2016). What having a "growth mindset" actually means. Harvard Business Review. Retrieved March 14, 2023, from https://hbr.org/2016/01/what-having-a-growth-mindset-actually-means

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0000.001
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 head0.075
GPT teacher head0.234
Teacher spread0.158 · 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.

Study designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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