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Record W2006899254 · doi:10.1037/h0088117

Review of The assertiveness workbook.

2001· article· en· W2006899254 on OpenAlexaboutno aff
Neil A. Rector

Bibliographic record

VenueCanadian Psychology/Psychologie canadienne · 2001
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsWorkbookPsychologyAssertivenessPsychotherapistPsychoanalysisAccounting

Abstract

fetched live from OpenAlex

RANDY J. PATERSON The Workbook Oakland, CA: New Harbinger Publications, 2000, 212 pages (ISBN 1-57224-2094, us$14.95, Softcover) Reviewed by NEIL RECTOR The ability to express our needs, wants, and feelings directly and honestly and to see the needs of others as equally important is the sine qua non of satisfying and effective relationships. Yet, many people become trapped within communication patterns that prevent self-expression. No doubt, these difficulties with communication have a negative impact on the person's ability to enjoy relationships and accomplish life goals. Difficulties with assertiveness may even represent a core vulnerability for severe psychopathology and contribute to the maintenance of social and occupational impairment. In this way, a clinically validated approach to helping people become more assertive would be extremely valuable. The Workbook integrates principles and strategies developed in an assertiveness training program at the Vancouver Hospital and is aimed at providing step-by-step self-help instruction. The book comprises 16 chapters in two sections: the first section is titled Understanding Assertiveness and includes defining and juxtaposing assertiveness with less-adaptive styles of communication - the passive, aggressive, and passive-aggressive styles. Paterson outlines the behaviours that characterize each of these styles, such as the avoidance of disagreement (passive), dismissing or ignoring the needs of others (aggressive), or the deliberate forgetting or delaying of a promised task (passive-aggressive). While we often think of assertiveness in terms of behaviours, Paterson presents a broader interpersonal model of assertiveness that highlights the dynamic interplay of beliefs, emotions, and behaviours that shape the context for assertiveness. For instance, beliefs such as other people are more important than me are likely to lead to behavioural passivity and feelings of helplessness and are just as important to target for improvement as the assertive behaviours themselves. Patterson is effective in getting across to the reader the cyclical nature of assertiveness (or its absence) through clinical case vignettes. The first chapter ends with a self-assessment section where readers are asked to record where, when, and with whom they are most likely to engage in passive, aggressive, or passive-aggressive styles of communication, and the perceived benefits if they could become more assertive in these contexts. After describing the multidimensional aspects of assertiveness, the remaining chapters in this section outline the barriers that may emerge as the person contemplates becoming more assertive. In Chapter 2, Patterson provides a cursory description of the role of stress and its impact on assertiveness, and provides a number of cognitive and behavioural strategies to cope with stress. Chapter 3 gives attention to the importance of gender role socialization in shaping the expectancies of assertiveness and the difficulties that may arise if the person is to become more assertive. Chapters 4 and 5 focus on the importance of underlying beliefs that influence passive, aggressive, and passive-aggressive styles. For instance, the belief that assertive means being selfish may predict a passive role, while the belief nice guys finish last may lead to an aggressive style. …

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0370.034

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 head0.044
GPT teacher head0.320
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2001
Admission routes1
Has abstractyes

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