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Record W1949201784

Making the most of our time.

2006· letter· en· W1949201784 on OpenAlexaboutno aff
Nick Pimlott

Bibliographic record

VenueGrower talks · 2006
Typeletter
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaMedicineReferralFamily medicineGuidelineAuditSurpriseGerontologyPsychologyDisease
DOInot available

Abstract

fetched live from OpenAlex

I enjoyed reading Dr Nazerali’s editorial in the February issue of Canadian Family Physician, as well as the accompanying articles. I have submitted the results of my own research in this area, but the timing was such that it will be published in a future issue of CFP. I led a group of researchers in the Dementia-NET group as we audited the practices of 160 family physicians in Ottawa, Ont; Toronto, Ont; and Calgary, Alta, to evaluate the extent to which family physicians follow the 48 key recommendations of the 1999 Canadian Consensus Conference on Dementia (CCCD). What we discovered, notwithstanding the limitations of chart audits, was interesting and perhaps disturbing. We found that family physicians had a very high referral rate (>80%), mostly to neurologists and geriatricians. This reflects, perhaps, family physicians’ lack of comfort in managing dementia or, perhaps, family members’ pressure to refer patients to specialists. We also discovered that few physicians assessed caregiver coping, which is a predictor of early institutionalization. Finally, few physicians assessed driving status and safety (about 13%). As a practising family doctor, however, these results do not surprise me, and they fit with some of the issues that Dr Nazerali raised in her editorial. First, time pressures are enormous for family physicians and are getting worse as we deal with more elderly patients with chronic illnesses. Second, the CCCD guidelines were passively disseminated with the Canadian Medical Association Journal, a sure-fire way to ensure that a guideline is ineffective. I agree that guidelines are very important in aiding family physicians to care for complex patients, but they need to be generated differently. We should not rely on a top-down approach from our specialist colleagues. There needs to be far greater input from family physicians about both content and process. There should also be more input from patients and their families. Further, passive dissemination does not work. Guideline makers need to develop tool kits that offer family physicians several options for implementation in their practices, as Dr Nazerali mentioned. Finally, there must be greater discussion, within the medical profession and within the community, about models of care. Among the options that need to be considered are shared-care models versus specialty-care models. The situation is becoming even more complex as primary care reform progresses. In family health teams, for example, which might have other providers available, the role of the family physician will need to be clarified. The next phase in our research, which we have just started, is to conduct focus groups with family physicians aimed at exploring all of the questions that Dr Nazerali raised in her editorial, including the role and structure of guidelines and models of care that might help family physicians to define and optimize their role in dementia care. We hope that over time our research will improve care for dementia patients and the lives of family physicians. Thanks for highlighting these important issues for Canadian family physicians.

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)0.005
metaresearch head score (Gemma)0.052
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: Commentary · Consensus signal: none
Teacher disagreement score0.107
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0050.004
Scholarly communication0.0090.007
Open science0.0030.006
Research integrity0.0070.014
Insufficient payload (model declined to judge)0.1070.104

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 head0.212
GPT teacher head0.463
Teacher spread0.251 · 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
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
Published2006
Admission routes1
Has abstractyes

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