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Record W4411863769 · doi:10.1177/00221678251350172

Love, Grief, and the Therapeutic Relationship: An Essay

2025· article· en· W4411863769 on OpenAlexaff
Elizabeth Mitchell

Bibliographic record

VenueJournal of Humanistic Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGriefPsychologyPsychoanalysisPsychotherapistTherapeutic relationshipEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

This essay explores the development of mutual and platonic love between a client and therapist. The author, a psychotherapist herself, provides a first-hand account of her experiences as a client in long-term relational psychotherapy, including reflection upon her grief regarding the relationship’s abrupt ending due to her therapist’s cancer diagnosis. Drawing upon Martin Buber’s concept of the I-Thou Relationship , as well as contemporary research surrounding the vital role of the therapeutic alliance in psychotherapy, this paper encourages therapists to fully consider the tensions that exist between the contractual versus human aspects of psychotherapy practice while embracing the inevitably relational nature of the work. To acknowledge love for one’s clients is complex and fraught with ethical and professional risks, and to fully attune to our clients’ love for us may be uncomfortable; however, as this essay suggests, there are also profound risks in dismissing, minimizing, or pathologizing these feelings. While remaining committed to necessary professional standards and boundaries, this author’s experience suggests that full recognition of the mutual love that may develop between client and therapist embodies a relational and feminist ethos to practice, may enhance therapy’s outcomes, and importantly, emphasizes the common humanity shared between both parties.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.016
Scholarly communication0.0070.006
Open science0.0010.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0020.001

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 head0.038
GPT teacher head0.418
Teacher spread0.380 · 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
Published2025
Admission routes1
Has abstractyes

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