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Record W4416344989 · doi:10.1080/2692398x.2025.2591970

Believe in Change: Ted Lasso and the Power of Narrative in Therapy

2025· article· en· W4416344989 on OpenAlexaff
Afarin Rajaei, C. Morton Hanna

Bibliographic record

VenueInternational Journal of Systemic Therapy · 2025
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsYorkville University
Fundersnot available
KeywordsNarrativePower (physics)Lasso (programming language)Feature (linguistics)

Abstract

fetched live from OpenAlex

This paper explores the therapeutic potential of the television series Ted Lasso ;(Apple TV, 2020–2023) through the lenses of narrative therapy and cinematherapy. Drawing on narrative inquiry and systemic analysis, it examines how televised storytelling can inform clinical reflection, relational insight, and emotional transformation. A synopsis of the series introduces readers to its central themes of optimism, leadership, and vulnerability, setting the stage for the narrative analysis that follows. The study identifies three interrelated themes that reflect therapeutic principles: redefining masculinity through emotional openness, promoting leadership rooted in empathy and emotional intelligence, and emphasizing relationships as catalysts for personal growth. A detailed narrative analysis, supported by specific episode and scene references, illustrates how Ted Lasso models systemic change through relational dialogue and vulnerability. By integrating current literature on narrative and film-based interventions, the paper demonstrates how popular media can be used intentionally in therapeutic and educational contexts to promote emotional literacy, reduce stigma, and enhance reflective practice. The discussion and clinical implications sections provide guidance for therapists, clients, and educators seeking to apply media-based interventions within systemic and culturally responsive frameworks.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.334
Teacher spread0.315 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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