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Record W4391695418 · doi:10.4018/978-1-6684-8607-8

Ethics for Mental Health Professionals

2024· book· en· W4391695418 on OpenAlexaff
S. Jack Olszewski

Bibliographic record

VenueAdvances in healthcare information systems and administration book series · 2024
Typebook
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsYorkville University
Fundersnot available
KeywordsMental healthHealth professionalsPsychologyEngineering ethicsNursingSociologyPolitical scienceMedicinePsychotherapistHealth careEngineeringLaw

Abstract

fetched live from OpenAlex

In the realm of mental health professionals, possessing knowledge of psychotherapeutic techniques is merely the tip of the iceberg. To excel in this field, professionals must master navigating the ethical challenges inherent in therapeutic relationships. Ethics for Mental Health Professionals illuminates the intricate landscape of ethical considerations that define the practice of mental health professionals. This book goes beyond the mere acquisition of psychotherapeutic techniques, emphasizing the paramount significance of navigating ethical challenges inherent in the field.This book delves into the ethical foundations underpinning the mental health profession, acknowledging the elevated responsibility and ethical requisites placed upon these professionals. Readers will embark on a journey transcending routine psychotherapeutic knowledge and exploring ethical decision-making within therapeutic relationships. The book addresses the critical need for mental health practitioners to possess clinical expertise and adeptly navigate ethical dilemmas that may arise in their professional journey. Mental health professionals will find invaluable insights within these pages. From dissecting the ethical dimensions of confidentiality to providing a roadmap for ethical decision-making when confronted with moral quandaries, this book equips practitioners with the tools to uphold the highest ethical standards in their practice.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.005
Open science0.0000.000
Research integrity0.0020.007
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 head0.098
GPT teacher head0.518
Teacher spread0.420 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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