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Record W2016023619 · doi:10.1176/pn.44.1.0011b

Several Obstacles Interfere With Alcohol Treatment

2009· article· en· W2016023619 on OpenAlexaboutno aff
Joan Arehart-Treichel

Bibliographic record

VenuePsychiatric News · 2009
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsDenialAddictionPsychologyAlcohol addictionPsychiatryMedicinePsychoanalysis

Abstract

fetched live from OpenAlex

Back to table of contents Previous article Next article Clinical & Research NewsFull AccessSeveral Obstacles Interfere With Alcohol TreatmentJoan Arehart-TreichelJoan Arehart-TreichelSearch for more papers by this authorPublished Online:2 Jan 2009https://doi.org/10.1176/pn.44.1.0011b“My father was a doctor and an alcoholic who died on skid row,” Graeme Cunningham, M.D., director of the Addiction Division of Homewood Health Center in Guelph, Ontario, reported at the Canadian Psychiatric Association meeting last September. “My mother was also an alcoholic. And I became one as well. For years I practiced medicine impaired, I was disciplined by my colleagues, I had lawsuits going against me. My 'M.D.' stood for 'master of denial.'”Such a denial is probably the biggest obstacle to helping alcoholic patients, Henry Kranzler, M.D., an addiction psychiatrist and associate scientific director of the University of Connecticut's Alcohol Research Center, said in an interview. And the reason is hardly surprising, he added—“Alcohol is widely used in society, is widely accepted, and people have a hard time seeing it for what it often can be, which is a real source of problems for people.”Another major hurdle involved in trying to help alcoholic patients, Kranzler noted, is that alcohol tends to be so reinforcing that many patients view it as a friend. “So it is very hard to get them to initially begin, or to subsequently stick with, quitting or with substantially reducing [their alcohol intake], depending on what their goals are.”A further challenge is engaging patients' families and friends in the endeavor, Marc Galanter, M.D., a professor of psychiatry and director of the Division of Alcoholism and Drug Abuse at New York University, said in an interview. He and his colleagues have developed a technique called“ network therapy” to facilitate such engagement. A book and video program about the technique, both titled Network Therapy for Alcohol and Drug Abuse, are available from American Psychiatric Publishing Inc. (More information about the book and video can be accessed at<www.appi.org>).Still another barrier to a successful outcome, Galanter continued, is that“ there is a real gulf between alcoholism rehab centers and practitioners in the communities where patients live. Let's say that someone receives residential treatment for alcoholism at Hazelden in Minnesota and afterward returns to his home in North Dakota. The continuity of care after discharge may not be very good, prompting him to relapse.”Finally, a frequent stumbling block to the rehabilitation of alcoholic patients is that health insurance plans may not cover alcoholism treatment, Galanter reported. However, the mental health insurance parity law passed this fall should help, he believes.Even in countries with universal health care insurance, alcoholism treatment is not always covered by health insurance. For example, two German researchers who have created a successful long-term alcohol recovery program called OLITA have failed to get either Germany's universal health insurance program or private health insurance plans to underwrite it. In fact, private insurance plans were especially opposed to the idea, one of the researchers—Hannelore Ehrenreich, M.D., Ph.D., a psychiatrist with the Max Planck Institute of Experimental Medicine in Goettingen—said in an interview.“They will not cover any disease that they believe is a person's own fault, and in their opinion, alcoholism falls in that category. They are 30 years behind the times.” ▪ ISSUES NewArchived

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.182
GPT teacher head0.430
Teacher spread0.248 · 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 designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

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