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Commentary on Mäkelä<i>et al</i>. (2011): How many patients must be asked about alcohol before it is enough?

2011· letter· en· W1937794462 on OpenAlexaboutno aff
Fredrik Spak

Bibliographic record

VenueAddiction · 2011
Typeletter
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)Psychological interventionCurriculumPopulationPublic healthMedicineBrief interventionPsychologyHealth careSocial psychologyNursingMedical educationFamily medicineEnvironmental healthPedagogyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Patients want to discuss, and they expect clinicians to discuss, drinking patterns. Clinical good will result if this challenge is taken, but the public health effects might be less impressive than is sometimes suggested. Mäkelä, Seppä & Havio [1] raise several interesting and important questions, among them whether alcohol issues are sufficiently attended to in health-care settings (HCS). For convenience, I will discuss this topic under the name ‘screening and brief intervention’ (SBI). The authors point to a number of vital issues, such as the general population's desire to discuss alcohol issues in HCS, and that fewer than 40% of those with heavy episodic drinking were advised about their drinking habits. There are a number of possible treatment barriers. For instance, Mäkeläet al. show that different groups in (Finnish) society are asked about alcohol in PHC at different rates; e.g. people with lower education are more seldom asked. This violates everyone's equal right to treatment. This can, to my mind, be due to class differences between patient and provider, perhaps mediated by language barriers. In order to promote SBI the authors propose increased alcohol education in the basic training of health-care staff. That is necessary, but not enough; unequal rights to treatment are just as important an issue to target in the training. Also, prevention in general needs to be given a stronger place in the curriculum, and this teaching should probably be integrated into the whole curriculum [2]. The authors argue rightly for more interventions on alcohol. There are at least two reasons to raise the alcohol issue in the consultations: (i) there may be a clinical impact of alcohol in the individual case; and (ii) alcohol is a public health issue. In a clinical case with (possible) alcohol involvement, it is difficult to excuse that alcohol is overlooked. However, the public health issue of SBI is more complicated. When HCS also focuses on risky consumption, before possible problems have developed, it takes on a public health perspective which is in accordance with the Ottawa Charter for Health Promotion [3]. In accordance with this intention, the world has seen an increased focus on life-style issues in health care, such as SBI. In Mäkeläet al.'s paper, the respondents were asked about their attitudes towards being asked about alcohol in HCS, and the issue is discussed from a public health perspective (or ‘population perspective’, as the authors refer to it). All measures undertaken in treatment, including SBI, are subject to priority discussions. SBI have some weaknesses. One can argue that SBI, although proven effective, have been difficult to implement, and as an individual-level policy SBI is a less effective strategy than population-level alcohol policies in preventing alcohol-related ill-health [4]; nor are SBI among the cheapest methods to reach that target. Following these observations, I doubt that SBI ever will have any major public health impact. Nevertheless, it should be implemented in HCS, but primarily for other reasons. An important question raised by this study is what proportion of patients should be screened. Is one-third enough or too few? How can we tell? Perhaps a more reasonable way to look at this issue is to ask ‘What is enough?’. Most would agree that 100% would be a waste of consultation time. Risky alcohol use is only one of many problems that HCS has to attend to, and it would not be feasible to ask all patients about everything. Figures of risky drinking vary around 10–15% in both the general population and in primary health care, so perhaps 30% is a useful figure; I believe it is good enough. As this comment is on science, should we not also question what that figure, a third, stands for? First, what does it say? When ranking what patients and doctors considered important with health encounters, offering preventive work was listed in 17th and 16th place, respectively [5]. This is not very high. Secondly, we know that it is difficult to recall remote alcohol intake. Perhaps it is equally difficult to recall what a doctor or nurse asked several months ago. In the Mäkelä study only the general population was studied. Perhaps a more elucidative result can be reached if both the patients and the doctors/nurses are asked, as also has been carried out [6]. If more patients wish to discuss the alcohol issue, one can assume that in many cases they could also do so. Missed opportunities cannot always be attributed to the care provider. I value greatly the ‘R’ in the FRAMES concept [7]. FRAMES is an acronym for Feed-back, Responsibility, Advice, Menu, Empathy and Self-efficacy, all vital components of a successful brief intervention activity. The ‘R’ emphasizes that the patient/client has responsibility for changing his/her life-style. Patient responsibility is well captured in the following sentences: ‘Asking questions and voicing your concerns’ and ‘Watching for problems and getting help solving them’[8]. There are several ways to increase the SBI activity in HCS. As a complement to stimulate care providers to pay attention to the issue, one could also assist patients (empowerment) to raise the subject of alcohol in the consultation with a doctor or a nurse, access an e-based intervention such as ‘Thrive’[9] or make a telephone call to a help-line. From a public health perspective, it could benefit SBI to stimulate citizens as well as patient organizations to request such service. Ass. Professor Spak receives funding from a university professorship.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.266
Teacher spread0.237 · 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
GenreCommentary

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

Citations4
Published2011
Admission routes1
Has abstractyes

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