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Record W2810379780 · doi:10.22329/celt.v11i0.4976

Managing the transition from concussion to return to learn in postsecondary education: strategies based on principles of UDL

2018· article· en· W2810379780 on OpenAlexaffvenue
Gail Frost, Maureen Connolly

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsBrock University
Fundersnot available
KeywordsConcussionPsychologyCognitionTask (project management)AccommodationWork (physics)Medical educationPoison controlInjury preventionMedicineEngineeringPsychiatry

Abstract

fetched live from OpenAlex

Concussion is a functional brain injury that can produce physical, cognitive, emotional and sleep-related symptoms. With correct management, most symptoms will resolve within a month and a gradual, progressive return to activity (cognitive and physical) that allows students to stay below the thresholds that make symptoms worse, can be started after the immediate post-impact rest period of 24-48 hours. The 6-step Return-to-Learn protocol works well to manage the return to the classroom for elementary and high school-aged students, however it is difficult to implement in a postsecondary setting, as it requires a level of monitoring not generally available through college or university student wellness centres. As a result, course instructors are often given the task of providing accommodations to help students recovering from concussion manage and master the content and complete the required work to pass their course. This paper will discuss the challenges facing the postsecondary student recovering from concussion and provide accommodation ideas and examples, with resources, that instructors may find helpful.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.384
Teacher spread0.346 · 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 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

Citations2
Published2018
Admission routes2
Has abstractyes

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