MétaCan
Menu
Back to cohort
Record W3134429417 · doi:10.1097/pra.0000000000000537

A Study in Contrasts: Technology Versus “Humanology”

2021· review· en· W3134429417 on OpenAlexaboutno aff
John M. Oldham

Bibliographic record

VenueJournal of Psychiatric Practice · 2021
Typereview
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthAnxietyHealth careDigital healthTelemedicinePsychologyMedicineComputer scienceMedical educationPsychiatryPolitical science

Abstract

fetched live from OpenAlex

March, 2021. In this issue of the Journal, Fowler and colleagues provide an elegant description of their increasing use of “integrated digital technologies” as part of routine psychiatric care. What they describe goes well beyond the now almost universal use of the electronic medical record, itself a “sea change” in standard medical practice. A crescendo of advances in the use of technology in general medical/surgical settings is happening at warp speed, such as the use of robotics, wearable bio-monitoring devices, imaging, big data, and many other innovative digital/technological strategies. This developing frontier has been less visible in the world of psychiatry and mental health care, but that is changing. Fowler and co-authors describe “a digital care navigation and data collection system, to integrate traditional … outcomes monitoring with novel biological monitoring between visits to provide patients and caregivers with real-time feedback on changes in symptoms such as stress, anxiety, and depression.” They present a 4-stage program that can be implemented in many types of mental health care, as well as in primary care. But the authors caution that as “alluring as technological innovations are, the focus must continually be brought back to the value of human contact and interaction in delivering quality care.” Also in this issue of the Journal, Villela and Lazar provide a Psychotherapy Guest Column entitled “Moving forward while standing still: A case of mental health advocacy evolving in the time of COVID-19.” Here, they describe an interesting recent challenge in Canada, where the Ontario Ministry of Health proposed to “radically limit psychotherapy provided by psychiatrists and family physicians,” modeled on managed care strategies in the United States. The argument rested on the view that time-limited cognitive-behavioral therapy is the only “evidence-based” type of psychotherapy that has been demonstrated to be effective, a position vociferously challenged by the authors. (See also an earlier Psychotherapy column by Plakun and Villela published in this Journal in 2019.1) Villela and Lazar refer to an opinion piece by Norman Doidge, MD, titled “In Ontario, a battle for the soul of psychiatry” and published in the Toronto Globe and Mail in April, 2019.2 In it, Doidge made a persuasive case that longer-term psychotherapy, funded by the Government in Ontario, is essential for patients who need it, and that it is both effective and cost-effective. Villela and Lazar contend that these conclusions have been demonstrated by peer-reviewed published evidence-based research, an argument also strongly endorsed by Eric Plakun in his introduction to this guest column. Although it may not be immediately apparent, here’s what, to me, links these 2 seemingly disparate contributions to this issue of the Journal. One aspect of the technological frontier is the development of computer-administered brief cognitive therapy, either alone, or assisted by a “mental health technician.” Let me quickly add my belief that this is an exciting exploration of the broadening potential to make effective therapy more widely available, a development particularly relevant in this era of COVID-19 with the burgeoning use of telemedicine and telepsychiatry. But each progressive step in the world of technomedicine must be taken with caution and these new techniques must be utilized appropriately. And in my opinion, nothing should ever replace the essential ingredient of compassionate mental health care—the capacity to connect, listen, and be there with and for our patients (someday face-to-face, instead of mask-to-mask). Hence my title, “Technology versus ‘Humanology’.” But perhaps I should have said “and” instead of “versus,” since we need both. John M. Oldham, MDEditor

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)0.013
metaresearch head score (Gemma)0.037
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: none
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.013
Scholarly communication0.0130.018
Open science0.0020.005
Research integrity0.0090.015
Insufficient payload (model declined to judge)0.0260.004

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 head0.100
GPT teacher head0.512
Teacher spread0.412 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Psychiatric PracticeSame topicPsychotherapy Techniques and ApplicationsFrench-language works237,207