Living with Anxiety: A Podcast Book Review of Dr. Catherine M. Pittman’s Rewire Your Anxious Brain: How to Use the Neuroscience of Fear to End Anxiety, Panic, and Worry
Bibliographic record
Abstract
This podcast reviews the book Rewire Your Anxious Brain: How to Use the Neuroscience of Fear to End Anxiety, Panic, and Worry co-authored by psychologist Dr. Catherine M. Pittman. Abstract: This book review will outline and analyze the core topics Dr. Pittman discusses, including the neuroscience behind anxiety production and where anxiety originates, to ultimately answer how rewiring the brain can help to prevent anxiety and its distressing effects. Discussing anxiety in the student experience, this analysis is situated locally and examines how resources at the University of Windsor implement anxiety prevention and rewiring techniques into services offered for students living with anxiety. Areas for improving the resources and support available for students living with the effects of an anxious brain will also be discussed. This book review will integrate into its analysis the United Nation's SDG 3, Good Health and Wellbeing, which aims to "ensure healthy lives and promote wellbeing for all at all ages" (United Nations). Anxiety is a fear response that can have debilitating, long-term effects on individuals; thus, to promote good health and wellbeing in one's life, rewiring the parts of the brain that overproduce anxiety is essential.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".