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Record W4385878739 · doi:10.3390/covid3080084

Scoping Review of Self-Directed Online Learning, Public School Students’ Mental Health, and COVID-19 in Noting Positive Psychosocial Outcomes with Self-Initiated Learning

2023· article· en· W4385878739 on OpenAlexaff
Carol Nash

Bibliographic record

VenueCOVID · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychosocialMental healthPsychologyAutodidacticismPublic healthCoronavirus disease 2019 (COVID-19)Medical educationClinical psychologyMedicinePsychiatryNursingMathematics educationDisease

Abstract

fetched live from OpenAlex

During COVID-19, self-directed learning, contrasted with standardized learning, became a necessary and promoted learning method in public schools—one potentially supportive of mental health regularly in public schools through the use of online learning. This is important because negative mental health has been classified as a global crisis, with the highest and lowest student achievers recognized as at greatest risk. Therefore, the conditions under which public school students’ mental health has been improved, leading to positive psychosocial outcomes, are relevant. Studies have identified that positive psychosocial outcomes in this regard require self-initiation of students’ self-directed learning. Also necessary is a reduction in the standardized expectations of parents to lead to positive psychosocial outcomes. Unknown is what research identifies the relevance of both self-initiated self-directed online learning and a reduction in parental expectations of standardization. To investigate this, self-directed learning, online learning, mental health, public schools, and COVID-19 were keywords searched following PRISMA guidelines for scoping reviews. The result: few returns considered either factor and those that did reinforce the need for both. The conclusion: self-initiated self-directed online learning supported by public schools and parents should be central in the aim of reducing the mental health crisis in students post COVID-19.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.094
GPT teacher head0.482
Teacher spread0.388 · 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 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

Citations10
Published2023
Admission routes1
Has abstractyes

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