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Record W1963733164 · doi:10.1111/add.12027

Letter in response to Mitchell <i>et al</i>. ‘Collective amnesia: reversing the global epidemic of addiction library closures’

2013· letter· en· W1963733164 on OpenAlexaffabout
Ivan Silver

Bibliographic record

VenueAddiction · 2013
Typeletter
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsAddictionPsychologyMental healthHealth carePublic relationsMedical educationSociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

I read this editorial 1 with great interest. I would like to thank the authors for bringing this issue to the addiction community's attention. As a Vice-President of Education in a large centre for addiction and mental health research, education, and treatment in Toronto, I would also like to express my concern regarding the recent trend to shrink library services in academic health science centres. As part of an education strategic planning process, I have engaged our librarians to be the lynchpins of life-long learning and knowledge transfer for our organization. Using this conceptual framework, librarians can be thought of as learning coaches. This includes a key role in training and professional development in information management for everyone who works in an academic health science centre. In order to be able to retrieve information, we all need training in the art of asking good questions by using models such as PICO 2. Librarians need to work closely with their information systems' leaders to develop the organization's specific models of access to information from multiple sources, decision support systems for clinicians, and personalized web pages for everyone working in an organization where evidence-based information can be pulled and where information is published on a regular basis. Hospitals are increasingly providing feedback to clinicians on their performance regarding their standard of patient care, and librarians are needed to help direct clinicians to sources of information that can help correct or improve their performance. In this way, librarians can also be conceptualized as knowledge brokers using a knowledge transfer framework. To optimize their role and function, libraries also need to be reconfigured to support work-based learning, including access to ‘just-in-time’ information that is distributed throughout the organization; in this case the library is as close as your desktop, laptop or your mobile device. It is crucial that library services adapt to the rapid changes that have recently democratized information retrieval, which make it much easier for anyone to do a literature search. However, this does not make everyone an information retrieval expert or a librarian. Working in the addictions field is complex; the approaches for clinical care, education and research are often multi-disciplinary and multi-systemic. Such levels of complexity require specialized support and services that are also complex and specialized, and available in-house and ‘just-in-time’. Libraries that can adapt their functioning to the needs of individuals working there, can play a key role in academic health science centres. Libraries should also maintain their long-established unique role as places for reflection and study for students, clinicians and scientists. Unlike libraries of old where silence was golden, the addictions library of the future might combine spaces of reflection and study with spaces for engagement of individuals and groups, idea and community-building, and knowledge exchange and discovery. Instead of running away from library services in addiction treatment and research facilities, my recommendation would be to run towards them. None.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.006
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.994
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0060.007
Open science0.0030.002
Research integrity0.0360.046
Insufficient payload (model declined to judge)0.0130.012

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 head0.061
GPT teacher head0.405
Teacher spread0.344 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2013
Admission routes2
Has abstractyes

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